Search

Background/Objectives: Emergency airway management is a critical skill for healthcare professionals, particularly in life-threatening situations like "cannot intubate, cannot oxygenate" (CICO) scenarios. Errors and delays in airway management can lead to adverse outcomes, including hypoxia and death. Cognitive aids, such as checklists and algorithms, have been proposed as tools to improve decision-making, procedural competency, and non-technical skills in these high-stakes environments. This systematic review aims to evaluate the effectiveness of cognitive aids in enhancing emergency airway management skills among health professionals and trainees. Methods: A systematic search of MEDLINE, Embase, CINAHL, Cochrane Library, Scopus, Web of Science, and ClinicalTrials.gov was conducted from February to March 2024. Studies examining the use of cognitive aids, such as the Vortex method, the ASA difficult airway algorithm, and visual airway aids, in emergency airway scenarios were included. Outcomes assessed included decision-making speed, procedural success rates, and non-technical skills. Data were extracted using standardized protocols, and the quality of included studies was appraised. Results: Five studies met inclusion criteria, encompassing randomized controlled trials, controlled studies, and mixed-methods research. Cognitive aids improved decision-making times (reduced by 44.6 s), increased procedural success rates, and enhanced non-technical skills such as teamwork and crisis management. Participants reported reduced anxiety and improved confidence levels (self-efficacy scores increased by 1.9 points). The Vortex method and visual cognitive aids demonstrated particular effectiveness in simulated scenarios. Conclusions: Cognitive aids significantly enhance emergency airway management skills, improving performance, reducing errors, and increasing provider confidence. Integrating cognitive aids into training programs has the potential to improve patient safety and outcomes. Further research is needed to validate these findings in clinical settings and optimize cognitive aid design and implementation.

Purpose: The phenomenon of avoidable care transitions has received increasing attention over the last decades due to its frequency and associated burden for the patients and the healthcare system. A number of assessment tools to identify avoidable transitions have been designed and implemented. The selection of the most appropriate tool appears to be challenging and time-consuming. This systematic review aimed to identify and comprehensively describe the assessment tools that can support stakeholders´ care transition decisions on older adults., Methods: This study was conducted as part of the TRANS-SENIOR research network. A systematic search was conducted in MEDLINE via PubMed, CINAHL, and CENTRAL. No restrictions regarding publication date and language were applied., Results: The search in three electronic databases revealed 1266 references and screening for eligibility resulted in 58 articles for inclusion. A total of 48 assessment tools were identified covering different concepts, judgement processes, and transition destinations. We found variation in the comprehensiveness of the tools with regard to dimensions used in the judgement process., Conclusion: All tools are not comprehensive with respect to the dimensions covered, as they address only one or a few perspectives. Although assessment tools can be useful in clinical practice, it is worth it to bear in mind that they are meant to support decision-making and supplement the care professional´s judgement, instead of replacing it. Our review might guide clinicians and researchers in choosing the right tool for identification of avoidable care transitions, and thus support informed decision-making., Competing Interests: Declarations. Conflict of interest: On behalf of all authors, the corresponding author states that there is no conflict of interest. Ethics approval and consent to participate: None reported., (© 2024. The Author(s).)

Introduction: Decision-support tools for detecting physiological deterioration are widely used in clinical medicine but have been criticised for fostering a task-oriented culture and reducing the emphasis on clinical reasoning. Little is understood about what influences clinical decisions aided by decision-support tools, including professional standards, policies, and contextual factors. Therefore, we explored management reasoning employed by anaesthesiologists and PACU nurses in the post-anaesthesia care unit during the discharge of high-risk postoperative patients., Methods: A qualitative constructivist study, conducting 18 semi-structured with 6 anaesthesiologists and 12 nurses across three Danish teaching hospitals. We analysed data through thematic analysis, utilising Michael Lipsky's theory of "street-level bureaucracy" in combination with David A. Cook's Management Reasoning Framework as a sensitising concept., Results: Standards are frequently ambiguous, requiring interpretation and prioritisation. This allows for professional discretion by circumventing established policies, reducing task-oriented culture and enhancing the clinical reasoning processes. However, discretion in management reasoning depends on whether the clinician is inclined to uphold or adjust policies to maintain professional standards, influencing discharge decisions., Conclusion: While decision-support tools offer cognitive aid and help standardise patient trajectories, they also limit professional discretion in management reasoning and can potentially compromise care and treatment. This highlights the need for a balanced approach that considers both the benefits and limitations of these tools in clinical decision-making., (© 2025 Acta Anaesthesiologica Scandinavica Foundation.)

Radiation oncology is among the most data-driven specialties in medicine. Recently, a wealth of peer-reviewed data has been published supporting the treatment of oligometastatic malignancies, demonstrating improved survival with metastasis-directed therapy, such as stereotactic body radiation therapy (SBRT), when combined with appropriate patient selection and treatment. However, there are currently few, if any, established guidelines that synthesize the abundance of data specific to radiotherapy into a single, easily accessed resource for clinicians. ACROPath® is a major initiative of the American College of Radiation Oncology (ACRO) that aims to present aggregated clinical pathway data in a highly usable format that is readily accessible to clinicians at the point of care in real time. The oligometastases pathway is the first published algorithm in this collection, with additional pathways anticipated in future publications. Clinical radiation oncologists with expertise in the treatment and management of oligometastatic disease were recruited from across ACRO's diverse membership, including both academic and private practice physicians, to ensure a broad-based experience and insight. Individual participants were assigned subsections of the pathway for guideline development, and then, each subsection was presented to the full group for evaluation and consensus development based on published data. Rather than presenting an unstructured set of treatment options, as is common in other treatment guidelines, this initiative aimed to categorize appropriate treatments based on published clinical evidence in a hierarchy further ranked by efficacy, toxicity, and cost. Based on these strata, treatment recommendations were collated and grouped into three rank categories (gold, silver, or bronze) to denote the degree of applicability. The team assembled an interactive document that will eventually be available online, and it is summarized in detail here. Recommendations are grouped both by the anatomic site of metastasis and by the primary tumor type, recognizing that original histology might impact the treatment differently in different anatomic locations. After a review of available published clinical evidence, the committee reached a consensus on all recommendations presented, categorizing each option as gold, silver, or bronze to guide clinicians appropriately. This first iteration of ACROPath® Oligometastases represents one of the few comprehensive clinical decision support tools available for managing patients with limited metastatic disease. It presents available data in a highly accessible, easily used reference, which will be formally reviewed and updated by the committee as frequently as emerging data requires, likely at six- to 12-month intervals., Competing Interests: Human subjects: All authors have confirmed that this study did not involve human participants or tissue. Animal subjects: All authors have confirmed that this study did not involve animal subjects or tissue. Conflicts of interest: In compliance with the ICMJE uniform disclosure form, all authors declare the following: Payment/services info: All authors have declared that no financial support was received from any organization for the submitted work. Financial relationships: Paul Wallner declare(s) personal fees from Nanocan Therapeutics. Medical advisory board. Tarita Thomas declare(s) personal fees from MJH Life Sciences. Paul Wallner, William Crook, and Evan Landau declare(s) employment from GenesisCare USA. Paul Wallner, former senior vice president for clinical affairs. Paul Wallner declare(s) employment from American Board of Radiology. Former associate executive director for radiation oncology. Christopher Jahraus declare(s) personal fees from Elekta. Site visit fees, consultant, advisory board member. Christopher Jahraus, Tarita Thomas, and Alexander Harris declare(s) non-financial support from American Medical Association. Served as ACRO delegate/advisor to RUC or CPT committees. Christopher Jahraus declare(s) personal fees from Blue Cross Blue Shield of Alabama. Former member of the preferred medical doctor advisory board. Bridget Koontz declare(s) non-financial support from Raythera Therapeutics, Inc. Board of directors. Bridget Koontz and Steven Finkelstein declare(s) personal fees from Blue Earth Diagnostics. Advisory board member. Steven Finkelstein declare(s) personal fees from Bayer. Consultant/speaker. Steven Finkelstein declare(s) personal fees from Johnson & Johnson. Consultant/speaker. Steven Finkelstein declare(s) personal fees from Novartis. Consultant/speaker. Christopher Jahraus declare(s) a patent from Salix Pharmaceuticals. Co-maker of a use patent on rifaximin. Steven Finkelstein declare(s) personal fees from Pfizer. Consultant/speaker. Steven Finkelstein declare(s) personal fees from Dendreon. Consultant/speaker. Bridget Koontz and Steven Finkelstein declare(s) personal fees from Lantheus. Advisory board member. Christopher Jahraus declare(s) personal fees from Radformation. Software access. Steven Finkelstein declare(s) personal fees from AstraZeneca. Consultant/speaker. Christopher Jahraus declare(s) personal fees from Fuse Oncology. Development collaboration. Christopher Jahraus declare(s) Expert witness in patent dispute. from Klarity Medical. Christopher Jahraus, Tarita Thomas, Dwight Heron, Paul Wallner, Bridget Koontz, and Steven Finkelstein declare(s) non-financial support from American College of Radiation Oncology. College leadership roles, including deputy editor of CURiE channel on Cureus platform (Jahraus). Intellectual property info: The American College of Radiation Oncology (ACRO) claims copyright (©) and trademark (™) 2024 over the name "ACROPath" and over all intellectual works and content described and presented as part of the ACROPath clinical pathways. All rights reserved by the American College of Radiation Oncology (ACRO). Other relationships: All authors have declared that there are no other relationships or activities that could appear to have influenced the submitted work., (Copyright © 2024, Jahraus et al.)

Over five hundred non-native ant species have spread worldwide, including many that have severe effects on biodiversity, are serious economic pests, or threaten human health and agriculture. The number of species in the Mediterranean is steadily increasing, with Italy being a prominent example. We provide risk screenings for non-native ant species in Italy using a Terrestrial Species Invasiveness Screening Kit using current climate conditions and future predictions. The screened species consist of 15 established and 12 horizon taxa. The results highlight the threat posed by Linepithema humile and Solenopsis invicta , followed by Wasmannia auropunctata , Brachyponera chinensis , and Solenopsis geminata . The threat posed by other tropical invaders such as Anoplolepis gracilipes and Pheidole megacephala depends on climate change scenarios. The Palearctic non-native Lasius neglectus and Tetramorium immigrans species are recognized as intermediate threats, while most screened species are far less threatening. The biology and ecology of most non-native ant species remain scarcely documented. Among the established species, B. chinensis , L. humile , and S. invicta deserve the most attention, while W. auropunctata is rapidly spreading in neighboring countries. Detection is still often accidental and late compared to establishment. Most species first establish around urban areas, making citizen science a promising tool for biosurveillance.

Most research on people undergoing lower limb amputations for dysvascular disease summarizes average patient outcome risks and average associations between patient factors and these outcomes. More recently, the importance of predicting patient-specific outcomes based on individual factors (ie, personalized rehabilitation) has become evident. This article reviews the evidence and discusses the importance of the following: (1) predicting outcomes to facilitate amputation-level and prosthesis prescription decisions and (2) how prediction models can be leveraged to develop decision support tools to facilitate provider/patient shared decision-making to ensure decisions considering each individual patient's priorities and preferences. Examples of these tools are discussed and referenced., Competing Interests: Disclosure No authors have any commercial or financial conflicts of interest nor any funding sources to report at the time of this submission., (Copyright © 2024 Elsevier Inc. All rights reserved.)

The WHO guidelines for classifying central nervous system (CNS) tumours are changing considerably with each release. The classification of CNS tumours is uniquely complex among most other solid tumours as it incorporates not just morphology, but also genetic and epigenetic features. Keeping current with these changes across medical fields can be challenging, even for clinical specialists. Large language models (LLMs) have demonstrated their ability to parse and process complex medical text, but their utility in neuro-oncology has not been systematically tested. We hypothesised that LLMs can effectively diagnose neuro-oncology cases from free-text histopathology reports according to the latest WHO guidelines. To test this hypothesis, we evaluated the performance of ChatGPT-4o, Claude-3.5-sonnet, and Llama3 across 30 challenging neuropathology cases, which each presented a complex mix of morphological and genetic information relevant to the diagnosis. Furthermore, we integrated these models with the latest WHO guidelines through Retrieval-Augmented Generation (RAG) and again assessed their diagnostic accuracy. Our data show that LLMs equipped with RAG, but not without RAG, can accurately diagnose the neuropathological tumour subtype in 90% of the tested cases. This study lays the groundwork for a new generation of computational tools that can assist neuropathologists in their daily reporting practice., (© 2024 The Author(s). The Journal of Pathology: Clinical Research published by The Pathological Society of Great Britain and Ireland and John Wiley & Sons Ltd.)

Background: Obtaining and describing semiology efficiently and classifying seizure types correctly are crucial for the diagnosis and treatment of epilepsy. Nevertheless, there exists an inadequacy in related informatics resources and decision support tools., Objective: We developed a symptom entity extraction tool and an epilepsy semiology ontology (ESO) and used machine learning to achieve an automated binary classification of epilepsy in this study., Methods: Using present history data of electronic health records from the Southwest Epilepsy Center in China, we constructed an ESO and a symptom-entity extraction tool to extract seizure duration, seizure symptoms, and seizure frequency from the unstructured text by combining manual annotation with natural language processing techniques. In addition, we achieved automatic classification of patients in the study cohort with high accuracy based on the extracted seizure feature data using multiple machine learning methods., Results: Data included present history from 10,925 cases between 2010 and 2020. Six annotators labeled a total of 2500 texts to obtain 5844 words of semiology and construct an ESO with 702 terms. Based on the ontology, the extraction tool achieved an accuracy rate of 85% in symptom extraction. Furthermore, we trained a stacking ensemble learning model combining XGBoost and random forest with an F1-score of 75.03%. The random forest model had the highest area under the curve (0.985)., Conclusions: This work demonstrated the feasibility of natural language processing-assisted structural extraction of epilepsy medical record texts and downstream tasks, providing open ontology resources for subsequent related work., (© Yilin Xia, Mengqiao He, Sijia Basang, Leihao Sha, Zijie Huang, Ling Jin, Yifei Duan, Yusha Tang, Hua Li, Wanlin Lai, Lei Chen. Originally published in JMIR Medical Informatics (https://medinform.jmir.org).)

Background: Primary care providers (PCPs) are often the first point of contact for discussing lung cancer screening (LCS) with patients. While guidelines recommend against screening people with limited life expectancy (LLE) who are less likely to benefit, these patients are regularly referred for LCS., Objective: We sought to understand barriers PCPs face to incorporating life expectancy into LCS decision-making for patients who otherwise meet eligibility criteria, and how a hypothetical point-of-care tool could support patient selection., Design: Qualitative study based on semi-structured telephone interviews., Participants: Thirty-one PCPs who refer patients for LCS, from six Veterans Health Administration facilities., Approach: We thematically analyzed interviews to understand how PCPs incorporated life expectancy into LCS decision-making and PCPs' receptivity to a point-of-care tool to support patient selection. Final themes were organized according to the Cabana et al. framework Why Don't Physicians Follow Clinical Practice Guidelines, capturing the influence of clinician knowledge, attitudes, and behavior on LCS appropriateness determinations., Key Results: PCP referrals to LCS for patients with LLE were influenced by limited knowledge of the life expectancy threshold at which patients are less likely to benefit from LCS, discomfort estimating life expectancy, fear of missing cancer at the point of early detection, and prioritization of factors such as quality of life, patient values, clinician-patient relationship, and family support. PCPs were receptive to a decision support tool to inform and communicate LCS appropriateness decisions if easy to use and integrated into clinical workflows., Conclusions: Our study suggests knowledge gaps and attitudes may drive decisions to offer screening despite LLE, a behavior counter to guideline recommendations. Integrating a LCS decision support tool that incorporates life expectancy within the electronic medical record and existing clinical workflows may be one acceptable solution to improve guideline concordance and increase confidence in selecting high benefit patients for LCS., (© 2024. The Author(s), under exclusive licence to Society of General Internal Medicine.)

Near infrared spectroscopy (NIRS) is an analytical technique that offers a real advantage over laboratory analysis in the food industry due to its low operating costs, rapid analysis, and non-destructive sampling technique. Numerous studies have shown the relevance of NIR spectra analysis for assessing certain food properties with the right calibration. This makes it useful in quality control and in the continuous monitoring of food processing. However, the NIR calibration process is difficult and time-consuming. Analysis methods and techniques vary according to the configuration of the NIR instrument, the sample to be analyzed and the attribute that is to be predicted. This makes calibration a challenge for many manufacturers. This paper aims to provide a data-driven methodology for developing a decision support tool based on the smart selection of NIRS wavelength to assess various food properties. The decision support tool based on the methodology has been evaluated on samples of cocoa beans, grains of wheat and mangoes. Promising results were obtained for each of the selected models for the moisture and fat content of cocoa beans (R 2 cv: 0.90, R 2 test: 0.93, RMSEP: 0.354%; R 2 cv: 0.73, R 2 test: 0.79, RMSEP: 0.913%), acidity and vitamin C content of mangoes (R 2 cv: 0.93, R 2 test: 0.97, RMSEP: 17.40%; R 2 cv: 0.66, R 2 test: 0.46, RMSEP: 0.848%), and protein content of wheat-DS2 (R 2 cv: 0.90, R 2 test:0.92, RMSEP: 0.490%) respectively. Moreover, the proposed approach allows results to be obtained that are better than benchmarks for the moisture and protein content of wheat-DS1 (R 2 cv: 0.90, R 2 test: 94, RMSEP: 0.337%; R 2 cv: 0.99, R 2 test: 0.99, RMSEP: 0.177%), respectively. PRACTICAL APPLICATION: This research introduces a practical tool aimed at determining the quality of food by identifying specific light wavelengths. However, it is important to acknowledge potential challenges, such as overfitting. Before implementation, it is crucial for further research to address and mitigate the issues to ensure the reliability and accuracy of the solution. If successfully applied, this tool could significantly enhance the accuracy of near-infrared spectroscopy in assessing food quality attributes. This advancement would provide invaluable support for decision-making in industries involved in food production, ultimately leading to better overall product quality for consumers., (© 2024 The Author(s). Journal of Food Science published by Wiley Periodicals LLC on behalf of Institute of Food Technologists.)

Background: Multiple clinician adjustable parameters impact upon glycemia in people with type 1 diabetes (T1D) using Medtronic Mini Med 780G (MM780G) AHCL. These include glucose targets, carbohydrate ratios (CR), and active insulin time (AIT). Algorithm-based decision support advising upon potential settings adjustments may enhance clinical decision-making., Methods: Single-arm, two-phase exploratory study developing decision support to commence and sustain AHCL. Participants commenced investigational MM780G, then 8 weeks Phase 1-initial optimization tool evaluation, involving algorithm-based decision support with weekly AIT and CR recommendations. Clinicians approved or rejected CR and AIT recommendations based on perceived safety per protocol. Co-design resulted in a refined algorithm evaluated in a further identically configured Phase 2. Phase 2 participants also transitioned to commercial MM780G following "Quick Start" (algorithm-derived tool determining initial AHCL settings using daily insulin dose and weight). We assessed efficacy, safety, and acceptability of decision support using glycemic metrics, and the proportion of accepted CR and AIT settings per phase., Results: Fifty three participants commenced Phase 1 (mean age 24.4; Hba1c 61.5mmol/7.7%). The proportion of CR and AIT accepted by clinicians increased between Phases 1 and 2 respectively: CR 89.2% vs. 98.6%, p < 0.01; AIT 95.2% vs. 99.3%, p < 0.01. Between Phases, mean glucose percentage time < 3.9mmol (< 70mg/dl) reduced (2.1% vs. 1.4%, p = 0.04); change in mean TIR 3.9-10mmol/L (70-180mg/dl) was not statistically significant: 72.9% ± 7.8 and 73.5% ± 8.6. Quick start resulted in stable TIR, and glycemic metrics compared to international guidelines., Conclusion: The co-designed decision support tools were able to deliver safe and effective therapy. They can potentially reduce the burden of diabetes management related decision making for both health care practitioners and patients., Trial Registration: Prospectively registered with Australia/New Zealand Clinical Trials Registry(ANZCTR) on 30th March 2021 as study ACTRN12621000360819., (© 2024. The Author(s).)

For successful development and adoption of technology on dairy farms, farmers need to be included in the innovation process. However, the design of agricultural technologies usually takes a top-down approach with little involvement of end-users at the early stages. Living Labs offer a methodology that involve end-users throughout the development process and emphasize the importance of understanding users' needs. Currently, exploration of dairy farmers' technology needs has been limited to specific types of technology (e.g., smartphone apps) and adult cattle. The aim of this study was to use a Living Lab approach to identify dairy farmers' data and technology needs to improve herd health and inform innovation development. We conducted 18 focus groups with a total of 80 dairy farmers from Belgium, Ireland, the Netherlands, Norway, Sweden, and the United Kingdom. Data were analyzed using Template Analysis, and 6 themes were generated representing the fundamental needs of autonomy, comfort, competence, community and relatedness, purpose, and security. Farmers favored technologies that provided them with convenience, facilitated their knowledge and understanding of problems on farm, and allowed them to be self-reliant. Issues with data sharing and accessibility and usability of software were barriers to technology use. Furthermore, farmers were facing problems around recruitment and management of labor and needed ways to reduce stress. Controlling aspects of the barn environment, such as air quality, hygiene, and stocking density, were particular concerns in relation to youngstock management. Overall, the findings suggest that developers of farm technologies may want to include farmers in the design process to ensure a positive user experience and improve accessibility. The needs identified in this study can be used as a framework when designing farm technologies to strengthen need satisfaction and reduce any potential harm toward needs., (© 2024, The Authors. Published by Elsevier Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).)

Clinical Bioinformatics is a knowledge framework required to interpret data of medical interest via computational methods. This area became of dramatic importance in precision oncology, fueled by cancer genomic profiling: most definitions of Molecular Tumor Boards require the presence of bioinformaticians. However, all available literature remained rather vague on what are the specific needs in terms of digital tools and expertise to tackle and interpret genomics data to assign novel targeted or biomarker-driven targeted therapies to cancer patients. To fill this gap, in this article, we present a catalog of software families and human skills required for the tumor board bioinformatician, with specific examples of real-world applications associated with each element presented., (© The Author(s) 2024. Published by Oxford University Press.)

Background: Childhood tumors in the central nervous system (CNS) have longer diagnostic delays than other pediatric tumors. Vague presenting symptoms pose a challenge in the diagnostic process; it has been indicated that patients and parents may be hesitant to seek help, and health care professionals (HCPs) may lack awareness and knowledge about clinical presentation. To raise awareness among HCPs, the Danish CNS tumor awareness initiative hjernetegn.dk was launched., Objective: This study aims to present the learnings from designing and implementing a decision support tool for HCPs to reduce diagnostic delay in childhood CNS tumors. The aims also include decisions regarding strategies for dissemination and use of social media, and an evaluation of the digital impact 6 months after launch., Methods: The phases of developing and implementing the tool include participatory co-creation workshops, designing the website and digital platforms, and implementing a press and media strategy. The digital impact of hjernetegn.dk was evaluated through website analytics and social media engagement., Implementation (results): hjernetegn.dk was launched in August 2023. The results after 6 months exceeded key performance indicators. The analysis showed a high number of website visitors and engagement, with a plateau reached 3 months after the initial launch. The LinkedIn campaign and Google Search strategy also generated a high number of impressions and clicks., Conclusions: The findings suggest that the initiative has been successfully integrated, raising awareness and providing a valuable tool for HCPs in diagnosing childhood CNS tumors. The study highlights the importance of interdisciplinary collaboration, co-creation, and ongoing community management, as well as broad dissemination strategies when introducing a digital support tool., (©Kathrine Synne Weile, René Mathiasen, Jeanette Falck Winther, Henrik Hasle, Louise Tram Henriksen. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 25.07.2024.)

Objective: To examine the use of decision support tools in decision making about antidepressants during conversations between patients with major depressive disorder (MDD) and their psychiatrists., Methods: Theme-oriented discourse analysis of two psychiatric consultation groups: control (n = 17) and intervention (n = 16). In the control group, only a doctor's conversation guide was used; in the intervention group, the conversation guide and a patient decision aid (PDA) were used., Results: Psychiatrists mainly dominated conversations in both consultation groups. They were less likely to elicit patient treatment-related perspectives in the intervention group as they focused more on delivering the information than obtaining patient perspectives. However, using PDA in the intervention group slightly encouraged patients to participate in decisional talk., Conclusion: The decision support tools did promote SDM performance. Using the conversation guide in both consultation groups encouraged the elicitation of patient perspectives, which helped the psychiatrists in tailoring their recommendations of options based on patient preferences and concerns. Using the PDA in the intervention group created space for treatment discussion and fostered active collaboration in treatment decision making., Practice Implications: Our findings have implications for SDM communication skills training and critical reflection on SDM practice., Competing Interests: Declaration of Competing Interest All authors disclose no actual or potential conflict of interest, including financial or personal relationships that could inappropriately influence or be perceived to influence their work., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Introduction: Digital clinical decision support (CDS) tools are of growing importance in supporting healthcare professionals in understanding complex clinical problems and arriving at decisions that improve patient outcomes. CDS tools are also increasingly used to improve antimicrobial stewardship (AMS) practices in healthcare settings. However, far fewer CDS tools are available in lowerand middle-income countries (LMICs) and in animal health settings, where their use in improving diagnostic and treatment decision-making is likely to have the greatest impact. The aim of this study was to evaluate digital CDS tools designed as a direct aid to support diagnosis and/or treatment decisionmaking, by reviewing their scope, functions, methodologies, and quality. Recommendations for the development of veterinary CDS tools in LMICs are then provided., Methods: The review considered studies and reports published between January 2017 and October 2023 in the English language in peer-reviewed and gray literature., Results: A total of 41 studies and reports detailing CDS tools were included in the final review, with 35 CDS tools designed for human healthcare settings and six tools for animal healthcare settings. Of the tools reviewed, the majority were deployed in high-income countries (80.5%). Support for AMS programs was a feature in 12 (29.3%) of the tools, with 10 tools in human healthcare settings. The capabilities of the CDS tools varied when reviewed against the GUIDES checklist., Discussion: We recommend a methodological approach for the development of veterinary CDS tools in LMICs predicated on securing sufficient and sustainable funding. Employing a multidisciplinary development team is an important first step. Developing standalone CDS tools using Bayesian algorithms based on local expert knowledge will provide users with rapid and reliable access to quality guidance on diagnoses and treatments. Such tools are likely to contribute to improved disease management on farms and reduce inappropriate antimicrobial use, thus supporting AMS practices in areas of high need., Competing Interests: HY, AH, and SB were employed by Ausvet. AC was employed by Ausvet Europe. The remaining author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Yusuf, Hillman, Stegeman, Cameron and Badger.)

The unprecedented challenges presented by the increase in global population have placed substantial demands on the livestock industry for human nutrition, necessitating heightened animal productivity and leading to an increased demand for natural resources to produce animal feed. Feed producers are leading the charge, consistently refining formulations to adapt to the evolving needs of livestock, driven in part by the cost of over 50% associated with feed production. This paper critically analyses the pressing issues within feed formulation, addressing the requirement for environmentally sustainable practices amidst the challenges of climate change. The exploration extends to how advanced decision support tools can enhance formulation techniques and profitability and contribute to environmental sustainability. Through an in-depth review of current feed formulation technologies, encompassing their applications and limitations, this study aims to enhance the existing knowledge base. Additionally, we examined future trends, highlighting the essential role of connecting technologies to establish a resilient and sustainable system. The emphasis is on the potential of formulation techniques to positively impact the environment and enhance the overall quality and performance of the animals. This paper provides actionable insights to improve animal production by examining feed formulation models and decision support tools. The anticipated outcome is a more informed and sustainable decision-making process, addressing the multifaceted challenges confronted by the livestock industry and making contributions to global efforts in climate change mitigation and environmental stewardship in animal production agriculture., Competing Interests: The authors declare no conflicts of interest.

Demands of domestic and foreign market specifications of carcass weight and fat cover, of beef cattle, have led to the development of cattle growth models that predict fat cover to assist on-farm managers make management decisions. The objectives of this paper are 4-fold: 1) conduct a brief review of the biological basis of adipose tissue accretion, 2) briefly review live and carcass assessments of beef cattle, and carcass grading systems used to develop quantitative compositional and quality indices, 3) review fat deposition models: Davis growth model ( DGM ), French National Institute for Agricultural Research growth model ( IGM ), Cornell Value Discovery System ( CVDS ), and BeefSpecs drafting tool ( BeefSpecsDT ), and 4) appraise the process of translating science and practical skills into research/decision support tools that assist the Beef industry improve profitability. The r 2 for live and carcass animal assessments, using several techniques across a range of species and traits, ranged from 0.61 to 0.99 and from 0.52 to 0.99, respectively. Model evaluations of DGM and IGM were conducted using Salers heifers (n = 24) and Angus-Hereford steers (n = 15) from an existing publication and model evaluations of CVDS and BeefSpecsDT were conducted using Angus steers (n = 33) from a research trial where steers were grain finished for 101 d in a commercial feedlot. Evaluating the observed and predicted fat mass ( FM ) is the focus of this review. The FM mean bias for Salers heifers were 7.5 and 1.3 kg and the root mean square error of prediction ( RMSEP ) were 31.2 and 27.8 kg and for Angus-Hereford steers the mean bias were -4.0 and -10.5 kg and the RMSEP were 9.14 and 21.5 kg for DGM and IGM, respectively. The FM mean bias for Angus steers were -5.61 and -2.93 kg and the RMSEP were 12.3 and 13.4 kg for CVDS and BeefSpecsDT, respectively. The decomposition for bias, slope, and deviance were 21%, 12%, and 68% and 5%, 4%, and 91% for CVDS and BeefSpecsDT, respectively. The modeling efficiencies were 0.38 and 0.27 and the models were within a 20 kg level of tolerance 91% and 88% for CVDS and BeefSpecsDT, respectively. Fat deposition models reported in this review have the potential to assist the beef industry make on-farm management decisions on live cattle before slaughter and improve profitability. Modelers need to continually assess and improve their models but with a caveat of 1) striving to minimize inputs, and 2) choosing on-farm inputs that are readily available., Competing Interests: The author declares no conflict of interest., (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Society of Animal Science.)

Globally, farmers are being increasingly encouraged to use technologies. Consequently, veterinarians often use farm data and technologies to provide farmers with advice. Yet very few studies have sought to understand veterinarians' perceptions of data and technologies on farms. The aim of this study was to understand veterinarians' experiences and opinions on data and technology on beef and dairy farms. An online qualitative survey was conducted with a convenience sample of 36 and 24 veterinarians from the United Kingdom and Ireland, respectively. The data were analysed using reflexive thematic analysis to generate four themes: (1) Improving veterinary advice through data; (2) Ensuring stock person skills are retained; (3) Longevity of technology; and (4) Solving social problems on farms. We show that technologies and data can make veterinarians feel more confident in the advice they give to farmers. However, the quality and quantity of data collected on cattle farms were highly variable. Furthermore, veterinarians were concerned that farmers can become over-reliant on technologies by not using their stockperson skills. As herd sizes increase, technologies can help to improve working conditions on farms with multiple employees of various skillsets. Veterinarians would like innovations that can help them to demonstrate their competence, influence farmers' behaviour, and ensure sustainability of the beef and dairy industries., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Objectives: To compare the performance of currently available biopsy decision support tools incorporating magnetic resonance imaging (MRI) findings in predicting clinically significant prostate cancer (csPCa)., Patients and Methods: We retrospectively included men who underwent prostate MRI and subsequent targeted and/or systematic prostate biopsies in two large European centres. Available decision support tools were identified by a PubMed search. Performance was assessed by calibration, discrimination, decision curve analysis (DCA) and numbers of biopsies avoided vs csPCa cases missed, before and after recalibration, at risk thresholds of 5%-20%., Results: A total of 940 men were included, 507 (54%) had csPCa. The median (interquartile range) age, prostate-specific antigen (PSA) level, and PSA density (PSAD) were 68 (63-72) years, 9 (7-15) ng/mL, and 0.20 (0.13-0.32) ng/mL 2 , respectively. In all, 18 multivariable risk calculators (MRI-RCs) and dichotomous biopsy decision strategies based on MRI findings and PSAD thresholds were assessed. The Van Leeuwen model and the Rotterdam Prostate Cancer Risk Calculator (RPCRC) had the best discriminative ability (area under the receiver operating characteristic curve 0.86) of the MRI-RCs that could be assessed in the whole cohort. DCA showed the highest clinical utility for the Van Leeuwen model, followed by the RPCRC. At the 10% threshold the Van Leeuwen model would avoid 22% of biopsies, missing 1.8% of csPCa, whilst the RPCRC would avoid 20% of biopsies, missing 2.6% of csPCas. These multivariable models outperformed all dichotomous decision strategies based only on MRI-findings and PSAD., Conclusions: Even in this high-risk cohort, biopsy decision support tools would avoid many prostate biopsies, whilst missing very few csPCa cases. The Van Leeuwen model had the highest clinical utility, followed by the RPCRC. These multivariable MRI-RCs outperformed and should be favoured over decision strategies based only on MRI and PSAD., (© 2023 The Authors. BJU International published by John Wiley & Sons Ltd on behalf of BJU International.)

The economic and methodological efficiencies of environmental DNA (eDNA) based survey approaches provide an unprecedented opportunity to assess and monitor aquatic environments. However, instances of inadequate communication from the scientific community about confidence levels, knowledge gaps, reliability, and appropriate parameters of eDNA-based methods have hindered their uptake in environmental monitoring programs and, in some cases, has created misperceptions or doubts in the management community. To help remedy this situation, scientists convened a session at the Second National Marine eDNA Workshop to discuss strategies for improving communications with managers. These include articulating the readiness of different eDNA applications, highlighting the strengths and limitations of eDNA tools for various applications or use cases, communicating uncertainties associated with specified uses transparently, and avoiding the exaggeration of exploratory and preliminary findings. Several key messages regarding implementation, limitations, and relationship to existing methods were prioritized. To be inclusive of the diverse managers, practitioners, and researchers, we and the other workshop participants propose the development of communication workflow plans, using RACI (Responsible, Accountable, Consulted, Informed) charts to clarify the roles of all pertinent individuals and parties and to minimize the chance for miscommunications. We also propose developing decision support tools such as Structured Decision-Making (SDM) to help balance the benefits of eDNA sampling with the inherent uncertainty, and developing an eDNA readiness scale to articulate the technological readiness of eDNA approaches for specific applications. These strategies will increase clarity and consistency regarding our understanding of the utility of eDNA-based methods, improve transparency, foster a common vision for confidently applying eDNA approaches, and enhance their benefit to the monitoring and assessment community., Competing Interests: CONFLICT OF INTEREST STATEMENT The authors declare no conflict of interest.

Tail biting (TB) in pigs is a complex issue that can be caused by multiple factors, making it difficult to determine the exact etiology on a case-by-case basis. As such, it is often difficult to pinpoint the reason, or set of reasons, for TB events, Decision Support Tools (DSTs) can be used to identify possible risk factors of TB on farms and provide suitable courses of action. The aim of this review was to identify DSTs that could be used to predict the risk of TB behavior. Additionally, technologies that can be used to support DSTs, with monitoring and tracking the prevalence of TB behaviors, are reviewed. Using the PRISMA methodology to identify sources, the applied selection process found nine DSTs related to TB in pigs. All support tools relied on secondary information, either by way of the scientific literature or expert opinions, to determine risk factors for TB predictions. Only one DST was validated by external sources, seven were self-assessed by original developers, and one presented no evidence of validation. This analysis better understands the limitations of DSTs and highlights an opportunity for the development of DSTs that rely on objective data derived from the environment, animals, and humans simultaneously to predict TB risks. Moreover, an opportunity exists for the incorporation of monitoring technologies for TB detection into a DST.

Compelling evidence shows that trees and greenspaces positively impact human well-being and the environment and offer economic benefits. Nevertheless, there exists a knowledge gap regarding the extent to which this evidence is efficiently incorporated into existing urban planning decision-making processes. This scoping review identified the extent to which urban planning decision-making frameworks, models, and tools consider the health, environmental, and economic benefits of trees and greenspace. Out of 28 reviewed studies, 11 (39%) reported on frameworks, models, and tools that take into account the health, environmental, and economic dimensions of trees and greenspace. Additionally, seven studies provided comprehensive coverage of at least one of the three key dimensions. However, none of the decision support frameworks, models, or tools comprehensively integrated all three dimensions, with only two tools (7%) scoring above 50% (five or more out of nine) in terms of comprehensiveness. This review highlights the urgent need to incorporate the true economic and monetary values of the health and environmental benefits of trees and greenspace to inform urban development decision making.

Environmental decision-making benefits from considering ecosystem services to ensure that aspects of the environment that people rely upon are fully evaluated. By focusing consideration of ecosystem services on final ecosystem goods and services (FEGS), the aspects of the environment directly enjoyed, used, or consumed by humans, these analyses can be more streamlined and effective. The U.S. Environmental Protection Agency has developed a set of tools to facilitate this consideration. The central feature of FEGS is that ecosystems are viewed through the diverse ways people directly benefit from them. The National Ecosystem Services Classification System (NESCS) Plus provides a framework for describing and identifying FEGS consistently. The standardization made available by NESCS Plus allows other tools and databases to interact using the NESCS Plus architecture and taxonomy, providing diverse insights for decision makers. Here, we examine the synergy of using the following four tools together: (1) the FEGS Scoping Tool; (2) the FEGS Metrics Report; (3) the EnviroAtlas; and (4) the EcoService Models Library. The FEGS Scoping Tool helps users determine what ecosystem services are relevant to a decision by harnessing FEGS understanding to enable communities to identify the relative importance of beneficiaries relevant to a decision and biophysical aspects of the environment of direct relevance to those beneficiaries. The FEGS Metrics Report can guide which metrics to monitor or model to represent those priority services. The EnviroAtlas, a powerful tool containing geospatial data and other resources related to ecosystem services, chemical and non-chemical stressors, and human health, and the EcoService Models Library, a database of ecosystem models, are two tools that support users in mapping and modeling endpoints relevant to priority services. While each of these tools is valuable on its own, together, they provide a powerful approach to easily incorporate and operationalize ecosystem services efforts into different parts of decision-making processes across different types of decisions. We illustrate how these integrated tools can be used together with a hypothetical example of a complex environmental management case study and the combined benefit of using the FEGS tools together., Competing Interests: Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

In this age of data, digital tools are widely promoted as having tremendous potential for enhancing food safety. However, the potential of these digital tools depends on the availability and quality of data, and a number of obstacles need to be overcome to achieve the goal of digitally enabled "smarter food safety" approaches. One key obstacle is that participants in the food system and in food safety often lack the willingness to share data, due to fears of data abuse, bad publicity, liability, and the need to keep certain data (e.g., human illness data) confidential. As these multifaceted concerns lead to tension between data utility and privacy, the solutions to these challenges need to be multifaceted. This review outlines the data needs in digital food safety systems, exemplified in different data categories and model types, and key concerns associated with sharing of food safety data, including confidentiality and privacy of shared data. To address the data privacy issue a combination of innovative strategies to protect privacy as well as legal protection against data abuse need to be pursued. Existing solutions for maximizing data utility, while not compromising data privacy, are discussed, most notably differential privacy and federated learning.

Globally, there is increasing interest in recovering resources from sanitation systems. However, the process of planning and implementing circular sanitation is complex and can necessitate software-based tools to support decision-making. In this paper, we review 24 decision support software tools used for sanitation planning, to generate insights into how they address resource recovery across the sanitation chain. The findings reveal that the tools can address many planning issues around resource recovery in sanitation including analysis of material flows, integrating resource recovery technologies and products in the design of sanitation systems, and assessing the sustainability implications of resource recovery. The results and recommendations presented here can guide users in the choice of different tools depending on, for example, what kind of tool features and functions the user is interested in as well as the elements of the planning process and the sanitation service chain that are in focus. However, some issues are not adequately covered and need improvements in the available tools including quantifying the demand for and value of resource recovery products, addressing retrofitting of existing sanitation infrastructure for resource recovery and assessing social impacts of resource recovery from a life cycle perspective. While there is scope to develop new tools or to modify existing ones to cover these gaps, communication efforts are needed to create awareness about existing tools, their functions and how they address resource recovery. It is also important to further integrate the available tools into infrastructure planning and programming processes by e.g. customizing to relevant planning regimes and procedures, to move them beyond research and pilots into practice, and hopefully contribute towards more circular sanitation systems., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Conflicts of interest often undermine conservation initiatives against biodiversity decline. Effective decision-making requires a deeper understanding of the positions, criteria, concerns, and perspectives of stakeholders. However, managing so many perspectives can be difficult, and if not done well, conflicts arise which make it difficult to achieve conservation goals. The purpose of this study is to demonstrate that identifying areas of consensus is a good starting point to generate more effective debates and address complex issues. To do this, we investigate the diversity of perspectives regarding biodiversity conservation schemes among stakeholders in the studied ports of Antwerp and Rotterdam. Using Q-methodology, a semi-quantitative technique that enables us to systematically study the subjective views of stakeholders involved in a topic, we identified and organized a range of shared perspectives into three groups, known as factors. A total of 20 participants sorted 45 statements according to their perceptions and objectives, from -4 'most disagreeable' to 4 'most agreeable'. Then, respondents explained their rankings in a post-sorting interview. Next, the data was analyzed quantitatively and qualitatively. The quantitative analysis was conducted in two parts: (i) Dividing respondents into groups based on similar perspectives and (ii) coupling distinguishing statements with one of the factors characteristic of that viewpoint. Finally, in a qualitative analysis, we used the distinguishing statements and insights from interviews to create narratives and titles for the three factors: (1) Ports are key for our economic wealth, hence port development should continue, (2) Nature first, and (3) Multi-actor governance. Our findings confirm consensuses in three areas: policy, land use, and mitigation tactics. Interestingly, all narratives unanimously agreed on the importance of regulating port development and land use changes via legislation and environmental impact assessments. However, they debated the rigidity of legislation and whether offsetting port expansion (and associated land and resource use claims) should take place locally or internationally. We also found that decision-making mostly followed a human-centered perspective, where economic values were more relevant than intrinsic ones. These insights can serve as a baseline for stakeholders to form coalitions around areas of consensus to depolarize debates and avoid decision-making gridlocks., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Objective: The aim of this study was to summarise the current knowledge of shared decision making (SDM) in patients facing a treatment decision about an intact abdominal aortic aneurysm (AAA), and to identify where further evidence is needed., Data Sources: MEDLINE, Embase, and the Cochrane Library were searched on 18 July 2021. An updated search was run on 31 May 2022 for relevant studies published from 1 January 2000 to 31 May 2022., Review Methods: This scoping review was undertaken in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines following a pre-defined protocol, retrieving studies reporting on aspects of SDM in those with intact AAAs. Qualitative synthesis of the articles was performed, and the results grouped according to theme., Results: Fifteen articles reporting on a total of 1 344 participants (age range 62-74 years) from hospital vascular surgery clinics with intact AAAs were included. Studies were observational (n = 9), non-randomised studies of an intervention (n = 3), and randomised clinical trials (n = 3). The first theme was the preferences and practice of SDM. The proportion of patients preferring SDM ranged from 58% to 95% (three studies), although objective rating of SDM practice was consistently < 50% (three studies). Clinician training improved SDM practice. The second theme was poor provision of information. Fewer than half of patients (0 - 46%) surveyed were informed about all available treatment options (three studies). Publicly available information sources were rated as poor. The third theme concerned the utility of decision making support tools (DSTs). Two randomised trials demonstrated that the provision of DSTs improves patient knowledge and agreement between patient preference and repair type received but not objective measures of SDM for patients with AAAs., Conclusion: SDM for patients with an intact AAA appears to be in its infancy. Most patients with an AAA want SDM, but this is not commonly applied. Most patients with an AAA do not receive adequate information for SDM, although the use of bespoke DSTs leaves patients better informed to facilitate SDM., (Copyright © 2023 The Author(s). Published by Elsevier B.V. All rights reserved.)

In applied ecology, numerical biophysical modelling allows running numerous simulations of spatial connectivity between source and destination locations. To characterize population connectivity, larval dispersal and resulting connectivity matrices can be computed for various forcing conditions of wind, density of spawners, or pelagic larval durations. Here, we investigate a methodology to synthetize meaningfully all numerical experiments performed for three atoll lagoons in the Tuamotu Archipelago pearl farming context. The objective is to identify the best restocking locations that consistently maximize the spread of pearl oyster larval dispersal, considering all forcing conditions. A multivariate generic approach is used to process and synthesize time-series of connectivity matrices and identify afterward with contextual criteria the spawning locations that match a variety of specific connectivity, logistical and ecological criteria. Similar synthesis of large volume of connectivity matrices will likely gain momentum considering the increasing use of numerical models for applied science and population management., Competing Interests: Declaration of competing interest The authors have no conflict of interests., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Since the Chernobyl disaster in 1986, decision support systems and modelling tools have been utilized in response to nuclear and radiological emergencies. The java-based real-time online decision support system (JRODOS) is a decision support tool that can be utilised in response to an emergency in managing off-site radiological consequences. This article documents the customization and use of JRODOS for Pakistan. JRODOS was tailored to the local Pakistan conditions, and a case study of a theoretical nuclear power plant accident was used to assess JRODOS's feasibility as a decision support tool. A worst-case probabilistic accident scenario was used to identify zones and areas where urgent protective actions, early protective actions and food restriction and other response actions could be required. The areas and distances identified for the implementation of protective and response actions for such a hypothetical accident were found to be in agreement with the emergency planning zones and distances suggested by the International Atomic Energy Agency (IAEA). Additionally, the implications of meteorological and source term input parameters on predicting the radiation doses to members of the public were investigated. It was identified that the output of such tools strongly depends on the availability and accuracy of the input parameters, such as radioactive release and meteorological data. Limitations and uncertainties associated with these tools need to be considered in deciding on protective and other response actions in response to a nuclear accident. As established by the IAEA, protective and other response actions need to be applied on a graded approach, taking into account the protection strategy and uncertainties and limitations in the available information and criteria, based on the conditions at the facility and off-site., (© 2023 Society for Radiological Protection. Published on behalf of SRP by IOP Publishing Limited. All rights reserved.)

Background: Fifty percent of people living with dementia are undiagnosed. The electronic health record (EHR) Risk of Alzheimer's and Dementia Assessment Rule (eRADAR) was developed to identify older adults at risk of having undiagnosed dementia using routinely collected clinical data., Objective: To externally validate eRADAR in two real-world healthcare systems, including examining performance over time and by race/ethnicity., Design: Retrospective cohort study PARTICIPANTS: 129,315 members of Kaiser Permanente Washington (KPWA), an integrated health system providing insurance coverage and medical care, and 13,444 primary care patients at University of California San Francisco Health (UCSF), an academic medical system, aged 65 years or older without prior EHR documentation of dementia diagnosis or medication., Main Measures: Performance of eRADAR scores, calculated annually from EHR data (including vital signs, diagnoses, medications, and utilization in the prior 2 years), for predicting EHR documentation of incident dementia diagnosis within 12 months., Key Results: A total of 7631 dementia diagnoses were observed at KPWA (11.1 per 1000 person-years) and 216 at UCSF (4.6 per 1000 person-years). The area under the curve was 0.84 (95% confidence interval: 0.84-0.85) at KPWA and 0.79 (0.76-0.82) at UCSF. Using the 90 th percentile as the cut point for identifying high-risk patients, sensitivity was 54% (53-56%) at KPWA and 44% (38-51%) at UCSF. Performance was similar over time, including across the transition from International Classification of Diseases, version 9 (ICD-9) to ICD-10 codes, and across racial/ethnic groups (though small samples limited precision in some groups)., Conclusions: eRADAR showed strong external validity for detecting undiagnosed dementia in two health systems with different patient populations and differential availability of external healthcare data for risk calculations. In this study, eRADAR demonstrated generalizability from a research sample to real-world clinical populations, transportability across health systems, robustness to temporal changes in healthcare, and similar performance across larger racial/ethnic groups., (© 2022. The Author(s), under exclusive licence to Society of General Internal Medicine.)

Locally advanced breast cancer (LABC) is a subset of breast cancer with locoregional progression without distant metastasis. The multimodality treatment (surgery, chemotherapy, radiotherapy, hormonal and targeted therapy if required) could significantly improve results in this specific group of patients. The complex and multiple options of treatment with similar mortality rates but different outcomes depending on the patient's desires, preferences and social environment require aid to facilitate the individual patient's decisions (e.g. Decision Aids (DAs) targeting patients considering primary or adjuvant treatment in LABC). In this context, DAs have been proven fundamental to help patients and clinicians share and agree on the best value option. The current systematic review aimed to evaluate the existing DAs related to these patients with LABC and identify current status and possible improvement areas (possible scarcity and heterogeneity of instruments, the status of their development, explanation of their purpose,…). No previous systematic reviews have been published on this topic. Following Prospero registration no: CRD42021286173, studies about LABC DAs were identified, without data or language restrictions, through a systematic search of bibliographic databases in December 2021. Quality was assessed using Qualsyst criteria (range 0.0-1.0). The quality of the 17 selected studies ranged from 0.46 to 0.95. Of them, 14/17 (82%) were DAs about treatment, only one (6%) about diagnosis, and 2/17 (12%) about the employment of DAs. No screening or follow-up DAs were retrieved. Twelve (70.6%) DAs were online tools. They varied broadly regarding their characteristics and purposes. Most of the studies focused on developing and testing different DAs (5/17; 29.4%) and their impact (7/17; 41.2%). Only 4/17 (23.5%) analysed their implementation and cost. These instruments have proven to improve patient's knowledge and decision-making, decrease patient anxiety, and patients tend to undergo treatment. However, nowadays, there is still a need for further research and consensus on methodology to develop practical DAs.

Introduction: When implementing interventions in primary care, tailoring implementation strategies to practice barriers can be effective, but additional work is needed to understand how to best select these strategies. This study sought to identify clinicians' contributions to the process of tailoring implementation strategies to barriers in clinical settings., Methods: We conducted a modified nominal group exercise involving 8 implementation scientists and 26 primary care clinicians in the WWAMI region Practice and Research Network. Each group identified implementation strategies it felt would best address barriers to using a cardiovascular disease (CVD) risk calculator previously identified across 44 primary care clinics from the Healthy Hearts Northwest pragmatic trial (2015 to 2018). These barriers had been mapped beforehand to the Consolidated Framework for Implementation Research (CFIR) domains. We examined similarities and differences in the strategies that 30% or more of each group identified (agreed-on strategies) for each barrier and for barriers in each CFIR domain. We used the results to demonstrate how strategies might be tailored to individual clinics., Results: Clinicians selected 23 implementation strategies to address 1 or more of the 13 barriers; implementation scientists selected 35. The 2 groups agreed on at least 1 strategy for barriers in each CFIR domain: Inner Setting, Outer Setting, Intervention Characteristics, Characteristics of Individuals, and Process. Conducting local needs assessment and assessing for readiness / identifying barriers and facilitators were the 2 most common implementation strategies chosen only by clinicians., Conclusions: Clinician stakeholders identified implementation strategies that augmented those chosen by implementation scientists, suggesting that codesign of implementation processes between implementation scientists and clinicians may strengthen the process of tailoring strategies to overcome implementation barriers., Competing Interests: Conflicts of interest: None., (© Copyright by the American Board of Family Medicine.)

Introduction: This pilot, randomized controlled trial aimed to evaluate the usability, among adolescents and young adults (AYAs) with ulcerative colitis (UC), of a web-based tool ('iBDecide') designed to facilitate shared decision making (SDM)., Methods: AYAs with UC (n = 35) were randomized to intervention (iBDecide, n = 14) and control (n = 12) arms before a scheduled clinic visit. We measured the usability of iBDecide, SDM, preferred decision-making style, decision conflict and intervention use., Results: Participants in the intervention group found iBDecide easy to use and agreed that it made them feel ready to participate in decision making and that they would use it to prepare for appointments. There were 130 visits to iBDecide, lasting on average 3 min, 41 s. The medication and nutrition trackers were among the most-viewed pages. Pages specifically designed to facilitate SDM were viewed only four times. Across groups, too few participants reported making decisions during clinic visits for decision-related measures to be reported., Conclusions: This pilot trial provides evidence for the usability of iBDecide and guidance for developing a larger-scale trial of a combined web-based and in-clinic SDM intervention. Overall, iBDecide shows promise in engaging AYAs with UC in SDM and condition management., Patient or Public Contribution: Patients, specifically AYAs with UC, and healthcare providers were involved in the design of this study's intervention, iBDecide. Additionally, the research team, from study conception to manuscript writing, included a young adult with inflammatory bowel disease., Clinical Trial Registration: This study was registered at clinicaltrials.gov (NCT04207008)., (© 2022 The Authors. Health Expectations published by John Wiley & Sons Ltd.)

Background: The treatment of depression in children and adolescents is a substantial public health challenge. This study examined artificial intelligence tools for the prediction of early outcomes in depressed children and adolescents treated with fluoxetine, duloxetine, or placebo., Methods: The study samples included training datasets (N = 271) from patients with major depressive disorder (MDD) treated with fluoxetine and testing datasets from patients with MDD treated with duloxetine (N = 255) or placebo (N = 265). Treatment trajectories were generated using probabilistic graphical models (PGMs). Unsupervised machine learning identified specific depressive symptom profiles and related thresholds of improvement during acute treatment., Results: Variation in six depressive symptoms (difficulty having fun, social withdrawal, excessive fatigue, irritability, low self-esteem, and depressed feelings) assessed with the Children's Depression Rating Scale-Revised at 4-6 weeks predicted treatment outcomes with fluoxetine at 10-12 weeks with an average accuracy of 73% in the training dataset. The same six symptoms predicted 10-12 week outcomes at 4-6 weeks in (a) duloxetine testing datasets with an average accuracy of 76% and (b) placebo-treated patients with accuracies of 67%. In placebo-treated patients, the accuracies of predicting response and remission were similar to antidepressants. Accuracies for predicting nonresponse to placebo treatment were significantly lower than antidepressants., Conclusions: PGMs provided clinically meaningful predictions in samples of depressed children and adolescents treated with fluoxetine or duloxetine. Future work should augment PGMs with biological data for refined predictions to guide the selection of pharmacological and psychotherapeutic treatment in children and adolescents with depression., (© 2022 The Authors. Journal of Child Psychology and Psychiatry published by John Wiley & Sons Ltd on behalf of Association for Child and Adolescent Mental Health.)

Background: Stroke represents the second preventable cause of death after cardiovascular disease and the third global cause of disability. In countries where national registries of the clinical quality of stroke care have been established, the publication and sharing of the collected data have led to an improvement in the quality of care and survival of patients. However, information on rehabilitation processes and outcomes is often lacking, and predictors of functional outcomes remain poorly explored. This paper describes a multicenter study protocol to implement a Stroke rehabilitation Registry, mainly based on a multidimensional assessment proposed by the Italian Society of Physical and Rehabilitation Medicine (PMIC2020), in a pilot Italian cohort of stroke survivors undergoing post-acute inpatient rehabilitation, to provide a systematic assessment of processes and outcomes and develop data-driven prediction models of functional outcomes., Methods: All patients with a diagnosis of ischemic or haemorrhagic stroke confirmed by clinical assessment, admitted to intensive rehabilitation units within 30 days from the acute event, aged 18+, and providing informed consent will be enrolled. Measures will be taken at admission (T0), at discharge (T1), and at follow-up, 3 months (T2) and 6 months (T3) after the stroke. Assessment variables include anamnestic data, clinical and nursing complexity information and measures of body structures and function, activity and participation (PMIC2020), rehabilitation interventions, adverse events and discharge data. The modified Barthel Index will be our primary outcome. In addition to classical biostatistical analysis, learning algorithms will be cross-validated to achieve data-driven prognosis prediction models., Conclusions: This study will test the feasibility of a stroke rehabilitation registry in the Italian health context and provide a systematic assessment of processes and outcomes for quality assessment and benchmarking. By the development of data-driven prediction models in stroke rehabilitation, this study will pave the way for the development of decision support tools for patient-oriented therapy planning and rehabilitation outcomes maximization., Clinical Tial Registration: The registration on ClinicalTrials.gov is ongoing and under review. The identification number will be provided when the review process will be completed., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Chiavilli, Campagnini, Baretta, Castagnoli, Paperini, Politi, Pellicciari, Baccini, Basagni, Marignani, Bardi, Sodero, Lombardi, Guolo, Navarro, Galeri, Montesano, Falco, Rovaris, Carrozza, Macchi, Mannini and Cecchi.)

Cognitive aids have been shown to facilitate adherence to evidence-based guidelines and improve technical performance of teams when managing simulated critical events. Few studies have explored the effect of cognitive aids on non-technical skills, such as teamwork and communication. The current study sought to explore the effects of different decision support tools (DST), a type of cognitive aid, on the technical and non-technical performance of teams. The current study represents a randomized, blinded, control trial of the effects of three versions of an electronic DST on team performance during multiple simulations of perioperative emergencies. The DSTs included a version with only technical information, a version with only non-technical information and a version with both technical and non-technical information. The technical performance of teams was improved when they used the technical DST and the combined technical and non-technical DST when compared to memory alone. The technical performance of teams was significantly worse when using the non-technical DST. All three versions of the DST had a negligible effect on the non-technical performance of teams. The technical performance of teams in the current study was affected by different versions of a DST, yet there was no effect on the teams' non-technical performance. The use of a DST, including those that focused on non-technical information, did not impact the non-technical performance of the teams., (© 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Several countries mandate informed or shared decision-making for low-dose CT (LDCT) lung cancer screening, but knowledge is limited about the type of information and presentation techniques used to support decision-making in practice. This review aimed to characterize the content, format, mode, and presentation methods of decision support tools (DSTs) for LDCT lung cancer screening. DSTs reported within peer-reviewed articles (January 2000-April 2021) were identified systematically from PubMed, PsycInfo, EMBASE, and CINAHL Plus. Inclusion criteria revolved around the development or evaluation of a resource or tool intended to support individual or shared decision-making for LDCT lung cancer screening. The data-charting and extraction framework was based on the International Patient Decision Aids Standards instrument and Template for Intervention Description and Reporting. Extracted data were organized within two categories: (1) study characteristics and context, format, and mode of DST use and (2) DST content and presentation methods. This review identified 22 DSTs in paper, video, or electronic formats across 26 articles. Most DSTs (n = 13) focused on knowledge exchange, whereas seven used interactive techniques to support values clarification (eg, Likert scales) and nine DSTs guided deliberation (eg, suggested discussion topics). The DSTs addressed similar topics, but the detail, quantification of probability, and presentation methods varied considerably. None described all the potential screening harms and results. The heterogeneity in DST design may affect the quality of decision-making, particularly for participants with lower literacy and numeracy. Evidence-based consensus guidelines for DST content and presentation methods should be developed collaboratively with screening-eligible adults., (Copyright © 2021 American College of Chest Physicians. Published by Elsevier Inc. All rights reserved.)

Objective: Different treatment options are available and feasible for various vascular surgical disorders. Hence, vascular surgery seems an area par excellence for shared decision making (SDM), in which clinicians incorporate the patient's preferences into the final treatment decision. However, current SDM levels in vascular surgical outpatient clinics are below expectations. To improve this, different decision support tools (DSTs) have been developed: online patient decision aids, consultation cards, and decision cards., Methods: This stepped wedge cluster randomised trial was conducted in 13 Dutch hospitals. Besides the developed DSTs, training on how to apply SDM during the clinician patient encounter was used in this study. Data were obtained via questionnaires and audio recordings. The primary outcome was the OPTION-5 score, an objective tool to assess the level of SDM, expressed as a percentage of exemplary performance. Main secondary outcomes were patients' disease specific knowledge, consultation duration, and treatment choice. Factors influencing OPTION-5 scores were studied using linear regression analysis., Results: Included in the study were 342 patients with an abdominal aortic aneurysm (AAA; n = 87), intermittent claudication (IC; n = 143), or varicose veins (VV; n = 112). Audiotapes of 395 consultations were analysed. Overall the mean OPTION-5 score significantly improved from 28.7% to 37.8% (mean difference 9.1%, 95% CI 6.5% - 11.8%) after implementation of the DSTs. Also, patient knowledge increased significantly (median increase: 13%, effect size: 0.13, p = .025). The number of patients choosing non-surgical treatment choices increased, with 21.4% to 28.8% for patients with AAA and doubled (16.0% to 32.0%) among patients with IC. For surgeons, the SDM training and for patients the decision aid significantly and independently increased OPTION-5 scores (p < .001 and p = .047, respectively)., Conclusion: Introducing DSTs improves the level of shared decision making in vascular surgery, improves patient knowledge, and shifts their preference towards more non-surgical treatments. The SDM training for clinicians and the decision aid for patients appeared the most effective means of improving SDM., Trial Registration: NTR6487., (Copyright © 2022 The Authors. Published by Elsevier B.V. All rights reserved.)

Background: Infection is more frequent, and serious in people aged > 65 as they experience non-specific signs and symptoms delaying diagnosis and prompt treatment. Monitoring signs and symptoms using decision support tools (DST) is one approach that could help improve early detection ensuring timely treatment and effective care., Objective: To identify and analyse decision support tools available to support detection of infection in older people (> 65 years)., Methods: A scoping review of the literature 2010-2021 following Arksey and O'Malley (2005) framework and PRISMA-ScR guidelines. A search of MEDLINE, Cochrane, EMBASE, PubMed, CINAHL, Scopus and PsycINFO using terms to identify decision support tools for detection of infection in people > 65 years was conducted, supplemented with manual searches., Results: Seventeen papers, reporting varying stages of development of different DSTs were analysed. DSTs largely focussed on specific types of infection i.e. urine, respiratory, sepsis and were frequently hospital based (n = 9) for use by physicians. Four DSTs had been developed in nursing homes and one a care home, two of which explored detection of non- specific infection., Conclusions: DSTs provide an opportunity to ensure a consistent approach to early detection of infection supporting prompt action and treatment, thus avoiding emergency hospital admissions. A lack of consideration regarding their implementation in practice means that any attempt to create an optimal validated and tested DST for infection detection will be impeded. This absence may ultimately affect the ability of the workforce to provide more effective and timely care, particularly during the current covid-19 pandemic., (© 2022. The Author(s).)

Social network analysis (SNA) tools and concepts are essential for addressing many environmental management and sustainability issues. One method to gather SNA data is to scrape them from environmental organizations' websites. Web-based research can provide important opportunities to understand environmental governance and policy networks while potentially reducing costs and time when compared to traditional survey and interview methods. A key parameter is 'search depth,' i.e., how many connected pages within a website to search for information. Existing research uses a variety of depths and no best practices exist, undermining research quality and case study comparability. We therefore analyze how search depth affects SNA data collection among environmental organizations, if results vary when organizations have different objectives, and how search depth affects social network structure. We find that scraping to a depth of three captures the majority of relevant network data regardless of an organization's focus. Stakeholder identification (i.e., who is in the network) may require less scraping, but this might under-represent network structure (i.e., who is connected). We also discuss how scraping web-pages of local programs of larger organizations may lead to uncertain results and how our work can combine with mixed methods approaches., Competing Interests: Competing interests The authors declare that they have no competing interests. This work has been reviewed in accordance with EPA policy and approved for publication. Mention of trade names or commercial products does not constitute endorsement or recommendation for use.

Rational and Objective: Requests for magnetic resonance imaging (MRI) exams have notably increased in Canada. However, many of these exams may not always be indicated. The Joint Department of Medical Imaging and the eReferral Program have worked collaboratively to embed an integrated clinical decision support (DS) tool within the eReferral process for diagnostic imaging requests. This retrospective chart review aimed to assess the necessity of MRI exams for knee pain patients at the point of referral in relation to the referral method (no DS tools within fax- vs. DS tools within eReferral)., Methods: Seven hundred and seventeen medical charts of routine MRI referral requests to an Ontario Hospital for patients with knee complaints were reviewed during the study period. The necessity of the MRI exams was evaluated using the supporting algorithm and knee pathway appropriateness guidelines. MRI exams were considered necessary if requested for symptoms or signs that align with best-practice standards, complemented with sound clinical assessment or history of a radiography scan before ordering an MRI., Results: In general, MRI requests made through eReferral were 13.289 times more likely to be necessary orders than those made through fax. The likelihood of referring patients for a necessary MRI exam was higher for eReferral than fax for the year 2018/2019 (53.0% vs. 26.8%, P < 0.001) and for the year 2019/2020 (58.5% vs. 16.3%, P < 0.001). In addition, the rate of ordering X-ray as the proper initial imaging scan for patients presenting with knee pain has steadily increased by 10% over the year for users of the eReferral platform compared to a decrease of 7% for those using fax., Conclusion: Our findings highlight the positive impact of integrating DS tools at the point of referral in supporting the ordering of necessary MRI scans, suggesting that service re-design and implementation of automated assistive technology services would impact patient care., (© 2021 John Wiley & Sons Ltd.)

Background: Approximately 1 in 5 patients feel unsatisfied after total knee arthroplasty (TKA). Prognostic tools may aid in the patient selection process and reduce the proportion of patients who experience unsatisfactory surgery. This study uses the prognostic tool SMART Choice (Patient Prognostic Tool for Total Knee Arthroplasty) to predict patient improvement after TKA. The tool aims to be used by the patient without clinician input and does not require clinical data such as X-ray findings or blood results. The objective of this study is to evaluate the SMART Choice tool on patient decision making, particularly willingness for surgery. We hypothesise that the use of the SMART Choice tool will influence willingness to undergo surgery, especially when used earlier in the patient TKA journey., Methods: This is a multicentred, pragmatic, randomised controlled trial conducted in Melbourne, Australia. Participants will be recruited from the St. Vincent's Hospital, Melbourne (SVHM) Orthopaedic Clinic, and the client base of HCF, Australia (private health insurance company). Patients over 45 years of age who have been diagnosed with knee osteoarthritis and considering TKA are eligible for participation. Participants will be randomised to either use the SMART Choice tool or treatment as usual. The SMART Choice tool provides users with a prediction for improvement or deterioration / no change after surgery based on utility score change calculated from the Veterans-RAND 12 (VR-12) survey. The primary outcome of the study is patient willingness for TKA surgery. The secondary outcomes include evaluating the optimal timing for tool use and using decision quality questionnaires to understand the patient experience when using the tool. Participants will be followed up for 6 months from the time of recruitment., Discussion: The SMART Choice tool has the potential to improve patient decision making for TKA. Although many prognostic tools have been developed for other areas of surgery, most are confined within academic bodies of work. This study will be one of the first to evaluate the impact of a prognostic tool on patient decision making using a prospective clinical trial, an important step in transitioning the tool for use in clinical practice., Trial Registration: Australia and New Zealand Clinical Trials Registry (ANZCTR) - ACTRN12622000072718 . Prospectively registered - 21 January 2022., (© 2022. The Author(s).)

Decision Support Systems (DSS) for Sustainable Urban Drainage Systems (SUDS) are a valuable aid for SUDS widespread adoption. These tools systematize the decision-making criteria and eliminate the bias inherent to expert judgment, abridging the technical aspect of SUDS for non-technical users and decision-makers. Through the collection and careful assessment of 120 papers on SUDS models and SUDS-DSS, this review shows how these tools are built, selected, and used to assist decision-makers questions. The manuscript classifies the DSS based on the question they assist in answering, the spatial scale used, the software selected, among other aspects. SUDS-DSS aspects that require more attention are identified, including environmental and social considerations, SUDS trains performance and criteria for selection, stochasticity of rainfall, and future scenarios impact. Suggestions for SUDS-DSS are finally offered to better equip decision-makers in facing emerging stormwater challenges in urban centers., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 The Authors. Published by Elsevier B.V. All rights reserved.)

Background: The objective of this study was to build models that define variables contributing to pneumonia risk by applying supervised Machine Learning-(ML) to medical and oral disease data to define key risk variables contributing to pneumonia emergence for any pneumonia/pneumonia subtypes., Methods: Retrospective medical and dental data were retrieved from Marshfield Clinic Health System's data warehouse and integrated electronic medical-dental health records (iEHR). Retrieved data were pre-processed prior to conducting analyses and included matching of cases to controls by (a) race/ethnicity and (b) 1:1 Case: Control ratio. Variables with >30% missing data were excluded from analysis. Datasets were divided into four subsets: (1) All Pneumonia (all cases and controls); (2) community (CAP)/healthcare associated (HCAP) pneumonias; (3) ventilator-associated (VAP)/hospital-acquired (HAP) pneumonias and (4) aspiration pneumonia (AP). Performance of five algorithms were compared across the four subsets: Naïve Bayes, Logistic Regression, Support Vector Machine (SVM), Multi-Layer Perceptron (MLP) and Random Forests. Feature (input variables) selection and ten-fold cross validation was performed on all the datasets. An evaluation set (10%) was extracted from the subsets for further validation. Model performance was evaluated in terms of total accuracy, sensitivity, specificity, F-measure, Mathews-correlation-coefficient and area under receiver operating characteristic curve (AUC)., Results: In total, 6,034 records (cases and controls) met eligibility for inclusion in the main dataset. After feature selection, the variables retained in the subsets were: All Pneumonia ( n = 29 variables), CAP-HCAP ( n = 26 variables); VAP-HAP ( n = 40 variables) and AP ( n = 37 variables), respectively. Variables retained ( n = 22) were common across all four pneumonia subsets. Of these, the number of missing teeth, periodontal status, periodontal pocket depth more than 5 mm and number of restored teeth contributed to all the subsets and were retained in the model. MLP outperformed other predictive models for All Pneumonia, CAP-HCAP and AP subsets, while SVM outperformed other models in VAP-HAP subset., Conclusion: This study validates previously described associations between poor oral health and pneumonia. Benefits of an integrated medical-dental record and care delivery environment for modeling pneumonia risk are highlighted. Based on findings, risk score development could inform referrals and follow-up in integrated healthcare delivery environment and coordinated patient management., Competing Interests: Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Modelling plays an important role in assessing disease risks and the efficacy of preventative actions. However, the extent to which existing models meet the needs of different groups of dairy farmers around disease prevention is unclear. A questionnaire gathered information on disease prevention actions undertaken by organic and conventional dairy farmers in Spain and the UK, and on their information preferences and needs in relation to such actions. A systematic review of animal health modelling articles was undertaken to compare the expressed needs of dairy farmers for information on disease prevention, with the focus and outputs of existing models. Farmer groups differed in needs when planning disease prevention interventions. Most farmers sourced animal health information from veterinarians. Farmers preferred to use practical experience to judge the efficacy of change. To fulfil the expressed needs of dairy farmers, models need to address specific farming contexts and non-economic impacts of change., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Isabel Blanco-Penedo reports travel was provided by National Institute for Agricultural and Food Research and Technology. Richard P. Kipling reports travel was provided by Biotechnology and Biological Sciences Research Council. This work started with the support of FACCE-JPI knowledge hub Modelling European Agriculture with Climate Change for Food Security, (© 2021 The Authors. Published by Elsevier Ltd.)

Spatial prioritization is a critical step in conservation planning, a process designed to ensure that limited resources are applied in ways that deliver the highest possible returns for biodiversity and human wellbeing. In practice, many spatial prioritizations fall short of their potential by focusing on places rather than actions, and by using data of snapshots of assets or threats rather than estimated impacts. We introduce spatial action mapping as an approach that overcomes these shortfalls. This approach produces a spatially explicit view of where and how much a given conservation action is likely to contribute to achieving stated conservation goals. Through seven case examples, we demonstrate simple to complex versions of how this method can be applied across local to global scales to inform decisions about a wide range of conservation actions and benefits. Spatial action mapping can support major improvements in efficient use of conservation resources and will reach its full potential as the quality of environmental, social, and economic datasets converge and conservation impact evaluations improve., (© 2021 The Authors. Annals of the New York Academy of Sciences published by Wiley Periodicals LLC on behalf of New York Academy of Sciences.)

The threat posed by invasive non-native species worldwide requires a global approach to identify which introduced species are likely to pose an elevated risk of impact to native species and ecosystems. To inform policy, stakeholders and management decisions on global threats to aquatic ecosystems, 195 assessors representing 120 risk assessment areas across all six inhabited continents screened 819 non-native species from 15 groups of aquatic organisms (freshwater, brackish, marine plants and animals) using the Aquatic Species Invasiveness Screening Kit. This multi-lingual decision-support tool for the risk screening of aquatic organisms provides assessors with risk scores for a species under current and future climate change conditions that, following a statistically based calibration, permits the accurate classification of species into high-, medium- and low-risk categories under current and predicted climate conditions. The 1730 screenings undertaken encompassed wide geographical areas (regions, political entities, parts thereof, water bodies, river basins, lake drainage basins, and marine regions), which permitted thresholds to be identified for almost all aquatic organismal groups screened as well as for tropical, temperate and continental climate classes, and for tropical and temperate marine ecoregions. In total, 33 species were identified as posing a 'very high risk' of being or becoming invasive, and the scores of several of these species under current climate increased under future climate conditions, primarily due to their wide thermal tolerances. The risk thresholds determined for taxonomic groups and climate zones provide a basis against which area-specific or climate-based calibrated thresholds may be interpreted. In turn, the risk rankings help decision-makers identify which species require an immediate 'rapid' management action (e.g. eradication, control) to avoid or mitigate adverse impacts, which require a full risk assessment, and which are to be restricted or banned with regard to importation and/or sale as ornamental or aquarium/fishery enhancement., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 The Authors. Published by Elsevier B.V. All rights reserved.)

Objective: Specific factors affecting generalizability of clinical prediction models are poorly understood. Our main objective was to investigate how measurement indicator variables affect external validity in clinical prediction models for predicting onset of vasopressor therapy., Design: We fit logistic regressions on retrospective cohorts to predict vasopressor onset using two classes of variables: seemingly objective clinical variables (vital signs and laboratory measurements) and more subjective variables denoting recency of measurements., Setting: Three cohorts from two tertiary-care academic hospitals in geographically distinct regions, spanning general inpatient and critical care settings., Patients: Each cohort consisted of adult patients (age greater than or equal to 18 yr at time of hospitalization), with lengths of stay between 6 and 600 hours, and who did not receive vasopressors in the first 6 hours of hospitalization or ICU admission. Models were developed on each of the three derivation cohorts and validated internally on the derivation cohort and externally on the other two cohorts., Interventions: None., Measurements and Main Results: The prevalence of vasopressors was 0.9% in the general inpatient cohort and 12.4% and 11.5% in the two critical care cohorts. Models utilizing both classes of variables performed the best in-sample, with C-statistics for predicting vasopressor onset in 4 hours of 0.862 (95% CI, 0.844-0.879), 0.822 (95% CI, 0.793-0.852), and 0.889 (95% CI, 0.880-0.898). Models solely using the subjective variables denoting measurement recency had poor external validity. However, these practice-driven variables helped adjust for differences between the two hospitals and led to more generalizable models using clinical variables., Conclusions: We developed and externally validated models for predicting the onset of vasopressors. We found that practice-specific features denoting measurement recency improved local performance and also led to more generalizable models if they are adjusted for during model development but discarded at validation. The role of practice-specific features such as measurement indicators in clinical prediction modeling should be carefully considered if the goal is to develop generalizable models., Competing Interests: Dr. Doshi-Velez consults for Davita Kidney Care and Google Health. The remaining authors have disclosed that they do not have any potential conflicts of interest., (Copyright © 2021 The Authors. Published by Wolters Kluwer Health, Inc. on behalf of the Society of Critical Care Medicine.)