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Context: With the high prevalence of anterior cruciate ligament rupture among young and active individuals, rehabilitation after the injury and surgery should meet the current evidence-based recommendations to restore knee function and reduce the risk of further injury. This study aimed to investigate the current rehabilitation practices and return to sports (RTS) criteria after anterior cruciate ligament reconstruction (ACLR) among physical therapists in Saudi Arabia. Design: Online cross-sectional survey-based study. Methods: A total of 177 physical therapists in Saudi Arabia participated in this survey. The survey included questions about the preferred postoperative timing and frequency of rehabilitation, current views on the importance of preoperative and postoperative rehabilitation to the overall outcomes, the timeframe of RTS, and the decision-making process to RTS. Results: Most therapists (96.6%) believed preoperative rehabilitation was essential/important to postoperative outcomes. Two-thirds encouraged patients to start rehabilitation program within 1 to 4 days immediately post-ACLR. RTS was permitted 6 to 9 months post-ACLR by 60% of therapists if satisfied with patient progress and capacity. Factors considered before RTS included knee strength (72.9%), functional capacity (86.4%), lower limb and trunk mechanics (62.7%), and psychological readiness (42.2%). Knee strength was assessed by manual muscle testing (39%), handheld dynamometry (15.3%), and isokinetic dynamometer (13.6%). While 60% of the therapists used single-limbed hop for distance for evaluating functional capacity, only 27.1% used a hop test battery. Conclusions: The surveyed physical therapists in Saudi Arabia demonstrated variations in the current rehabilitation practices and RTS criteria post-ACLR. Over half of the surveyed therapists considered preoperative rehabilitation essential to postoperative outcomes. However, the therapists should update their perspective with current evidence-based practice regarding the RTS timeframe, psychological readiness assessment for RTS, and knee strength evaluation using objective measurements. [ABSTRACT FROM AUTHOR]

Background/Objective: This study aimed to validate the eight-item Suicidality Scale (SS) in Spanish in a Colombian sample to aid in suicide risk assessment, given the pressing need for accurate, accessible tools in resource-strained settings. Methods: A sample of 313 participants, drawn from both clinical and community settings, was used to evaluate the psychometric properties of the SS through tests of internal consistency, item response theory (IRT), and comparisons with clinical risk evaluations. Results: The SS demonstrated strong psychometric properties, with high internal consistency (ω = 0.96) and a significant correlation with clinical risk assessments (r = 0.84). Model fit indices confirmed a unidimensional eight-item structure with low error rates, while item response analysis revealed strong item discrimination. No differential item functioning was observed by gender or psychiatric diagnosis, supporting its consistency across demographics. Items on past suicide attempts and desire to live were excluded as they did not improve scale performance. Variability within risk levels suggests that individual differences may require clinical judgment. Conclusions: The findings validate the Colombian–Spanish SS as a valuable tool for suicide risk assessment, usable in both self-report and clinician-administered formats. Its brief, culturally adapted structure supports its utility in resource-limited environments, providing an accessible option for rapid screening. While the SS effectively categorizes general risk, further longitudinal studies are recommended to enhance its applicability in guiding clinical decisions and long-term risk management. [ABSTRACT FROM AUTHOR]

Clonus is characterized by involuntary, rhythmic, oscillatory muscle contractions, typically triggered by rapid muscle stretching and is frequently associated with spastic equinovarus foot (SEVF), where it may increase risk of falls and cause discomfort, pain, and sleep disorders. We hypothesize that selective diagnostic nerve block (DNB) of the tibial nerve motor branches can help identify which muscle is primarily responsible for clonus in patients with SEVF and provide useful information for botulinum neurotoxin type A (BoNT-A) treatment. This retrospective study explored which calf muscles contributed to clonus in 91 patients with SEFV after stroke (n = 31), multiple sclerosis (n = 21), and cerebral palsy (n = 39), using selective DNB. We found that SEVF-associated clonus was most commonly driven by the soleus muscle, followed by the gastrocnemius lateralis and medialis, tibialis posterior, and flexor digitorum longus, and that frequency differed according to SEVF etiology. Our data suggest that identifying the muscles involved in SEVF-associated clonus may aid clinicians in personalizing BoNT-A treatment to single patients. Also, the findings of this study suggest that applying a 'stroke model' to treating spasticity secondary to other etiologies may not always be appropriate. [ABSTRACT FROM AUTHOR]

Leptomeningeal collaterals (LMCs) are crucial in mitigating the impact of acute ischemic stroke (AIS) by providing alternate blood flow routes when primary arteries are obstructed. This article explores the evolutionary pathophysiology of LMCs, highlighting their critical function in stroke and the genetic and molecular mechanisms governing their development and remodelling. We address the translational challenges of applying animal model findings to human clinical scenarios, emphasizing the need for further research to validate emerging therapies—such as pharmacological agents, gene therapy and mechanical interventions—in clinical settings, aimed at enhancing collateral perfusion. Computational modelling emerges as a promising method for integrating experimental data, which requires precise parameterization and empirical validation. We introduce the 'Evolucollateral Dynamics' hypothesis, proposing a novel framework that incorporates evolutionary biology principles into therapeutic strategies, offering new perspectives on enhancing collateral circulation. This hypothesis emphasizes the role of genetic predispositions and environmental influences on collateral circulation, which may impact therapeutic strategies and optimize treatment outcomes. Future research must incorporate human clinical data to create robust treatment protocols, thereby maximizing the therapeutic potential of LMCs and improving outcomes for stroke patients. [ABSTRACT FROM AUTHOR]

Background: The interventions performed in most randomized controlled trials (RCTs) on neck pain patients are standardized, irrespective of the high heterogeneity of patients. However, clinicians tend to choose an intervention based on the patients' clinical characteristics, and thus match the treatment to the patient. Objectives: To investigate the effectiveness of interventions matched to the clinical characteristics of patients with neck pain versus the same, but unmatched treatment for improving pain or disability. Design: A systematic review and meta-analysis conducted following Cochrane guidelines Methods: Databases searches were performed from inception to September 2023. RCTs were included if the patients in the experimental group received a treatment matched to clinical presentation or to clinicians' assessment, if the patients in the control group received a similar but unmatched treatment, and if pain or disability were reported as outcome measures. Results: The literature search produced 9516 records of which 27 met the inclusion criteria. Matched exercise therapy was superior to unmatched exercise for pain (SMD −0.57; 95% CI −0.95, −0.18) and for disability (SMD −0.69; 95% CI −1.14, −0.23) at short term, but not at intermediate-term follow-up. Matched manual treatment was not superior to unmatched manual therapy for pain or for disability at short or intermediate-term follow-up. Conclusions: Results suggest that matching exercise to movement limitation, trapezius myalgia, or forward head position may lead to better outcomes in the short term, but not in the intermediate-term. Matched manual therapy was not superior to unmatched treatment either short or intermediate-term. Further research is warranted to verify if those criteria are potentially useful matching criteria. [ABSTRACT FROM AUTHOR]

Drug sustainability (DS), a surrogate marker for drug efficacy, is important, especially when aiming for precision medicine. However, it lacks reliable prediction methods. To develop and externally validate a web-based artificial intelligence(AI)-derived tool for predicting DS of infliximab and vedolizumab in patients with moderate-to-severe Ulcerative Colitis (UC). Data from three Israeli centers included infliximab or vedolizumab patients treated for >54 weeks. Sustainability meant no corticosteroids, hospitalizations or surgeries. Machine learning techniques predicted >54-week and overall DS using baseline clinical data. The model was developed using data from 246 patients from Rabin Medical Center and externally validated on 67 patients from Rambam Health Care Campus and Sheba Medical Center. No significant difference in DS was observed across the datasets. Most patients were biologic-naïve and primarily treated with vedolizumab. The model performed well, with an area under the ROC curve of 0.86, and showed good accuracy (65.5 %-76.9 %) across the test sets. The study introduces a novel, AI-based tool for predicting >54-week DS of infliximab and vedolizumab in moderate-to-severe UC, using baseline parameters. This can aid clinical decision-making in the framework of precision medicine, promising to optimize disease management while maintaining physician autonomy. [ABSTRACT FROM AUTHOR]

This study aims to explore the efficacy of Bayesian Neural Networks (BNNs) in enhancing predictive modeling for healthcare applications. Advancements in artificial intelligence have significantly improved predictive modeling capabilities, with BNNs offering a probabilistic framework that addresses the inherent uncertainty and variability in healthcare data. This study demonstrates the real-world applicability of BNNs through three key case studies: personalized diabetes treatment, early Alzheimer's disease detection, and predictive modeling for HbA1c levels. By leveraging the Bayesian approach, these models provide not only enhanced predictive accuracy but also uncertainty quantification, a critical factor in clinical decision making. While the findings are promising, future research should focus on optimizing scalability and integration for real-world applications. This work lays a foundation for future studies, including the development of rating scales based on BNN predictions to improve clinical outcomes. [ABSTRACT FROM AUTHOR]

Background: Clinical decision-making (CDM) refers to physicians' ability to gather, evaluate, and interpret relevant diagnostic information. An integral component of CDM is the medical history conversation, traditionally practiced on real or simulated patients. In this study, we explored the potential of using Large Language Models (LLM) to simulate patient-doctor interactions and provide structured feedback. Methods: We developed AI prompts to simulate patients with different symptoms, engaging in realistic medical history conversations. In our double-blind randomized design, the control group participated in simulated medical history conversations with AI patients (control group), while the intervention group, in addition to simulated conversations, also received AI-generated feedback on their performances (feedback group). We examined the influence of feedback based on their CDM performance, which was evaluated by two raters (ICC = 0.924) using the Clinical Reasoning Indicator – History Taking Inventory (CRI-HTI). The data was analyzed using an ANOVA for repeated measures. Results: Our final sample included 21 medical students (agemean = 22.10 years, semestermean = 4, 14 females). At baseline, the feedback group (mean = 3.28 ± 0.09 [standard deviation]) and the control group (3.21 ± 0.08) achieved similar CRI-HTI scores, indicating successful randomization. After only four training sessions, the feedback group (3.60 ± 0.13) outperformed the control group (3.02 ± 0.12), F (1,18) = 4.44, p =.049 with a strong effect size, partial η2 = 0.198. Specifically, the feedback group showed improvements in the subdomains of CDM of creating context (p =.046) and securing information (p =.018), while their ability to focus questions did not improve significantly (p =.265). Conclusion: The results suggest that AI-simulated medical history conversations can support CDM training, especially when combined with structured feedback. Such training format may serve as a cost-effective supplement to existing training methods, better preparing students for real medical history conversations. [ABSTRACT FROM AUTHOR]

Simple Summary: This review explores the significant differences in the diagnosis, treatment, and outcomes of urologic cancers, including prostate, bladder, kidney, and testicular cancers, among different population groups. By examining factors like race, socioeconomic status, and access to specialized care, we aim to highlight the disparities that exist in urologic oncology and suggest strategies to ensure that all patients, regardless of their background, receive equitable treatment. The findings from this study are crucial for clinicians, policymakers, and researchers to understand the barriers faced by underserved populations and to develop interventions that can improve cancer care for everyone. Health disparities in urologic oncology, particularly in prostate, bladder, kidney, and testicular cancers, significantly impact patient outcomes across different demographic groups. This narrative review aims to investigate the extent and drivers of these disparities, focusing on the influence of race, socioeconomic status, and geographic location on diagnosis, treatment, and survival outcomes. We conducted a comprehensive review of the existing literature and analyzed data from national cancer databases to identify patterns of inequity. Our findings reveal that minority populations, individuals with lower socioeconomic status, and those residing in underserved areas are less likely to receive timely and guideline-based care, leading to worse outcomes. This review underscores the urgent need for targeted interventions, including policy reforms, health system restructuring, enhanced community outreach, and increased funding for disparity-focused research, to ensure equitable access to high-quality oncologic care. Addressing these disparities is crucial for improving cancer outcomes and achieving health equity in urologic oncology. [ABSTRACT FROM AUTHOR]

With the increasing rate of infections caused by multidrug‐resistant organisms (MDRO), selecting appropriate empiric antibiotics has become challenging. We aimed to develop and externally validate a model for predicting the risk of MDRO infections in patients with cirrhosis. Methods: We included patients with cirrhosis and bacterial infections from two prospective studies: a transcontinental study was used for model development and internal validation (n = 1302), and a study from Argentina and Uruguay was used for external validation (n = 472). All predictors were measured at the time of infection. Both culture‐positive and culture‐negative infections were included. The model was developed using logistic regression with backward stepwise predictor selection. We externally validated the optimism‐adjusted model using calibration and discrimination statistics and evaluated its clinical utility. Results: The prevalence of MDRO infections was 19% and 22% in the development and external validation datasets, respectively. The model's predictors were sex, prior antibiotic use, type and site of infection, MELD‐Na, use of vasopressors, acute‐on‐chronic liver failure, and interaction terms. Upon external validation, the calibration slope was 77 (95% CI.48–1.05), and the area under the ROC curve was.68 (95% CI.61–.73). The application of the model significantly changed the post‐test probability of having an MDRO infection, identifying patients with nosocomial infection at very low risk (8%) and patients with community‐acquired infections at significant risk (36%). Conclusion: This model achieved adequate performance and could be used to improve the selection of empiric antibiotics, aligning with other antibiotic stewardship program strategies. [ABSTRACT FROM AUTHOR]

Aim: To explore and describe acute care nurses' decisions to recognise and respond to improvement in patients' clinical states as they occurred in the real‐world clinical environment. Design: A descriptive study. Methods: Nine medical and eleven surgical nurses in a large Australian metropolitan hospital were individually observed during nurse–patient interactions and followed up in interview to describe their reasoning and clinical judgements behind observed decisions. Verbal description of observations and interviews were recorded and transcribed. Reflexive thematic analysis was used to analyse the data. Results: The three themes constructed from the data were as follows: nurses checking in; nurses reaching judgements about improvements; and nurses deciding on the best person to respond. Acute care nurses made targeted assessment decisions based on predicted safety risks related to improvement in clinical states. Subjective and objective cues were used to assess for and make judgements about patient improvement. Acute care nurses' judgment of patient safety and a desire to promote patient centred care guided their decisions to select the appropriate person to manage improvement. Conclusions: The outcomes of this research have demonstrated that the proven safety benefits of acute care nurses' decision making in response to deterioration extend to improvement in patients' clinical states. In response to improvement, acute care nurses' decisions protect patients from harm and promote recovery. Implications for patient care: Early recognition and response to improvement enable acute care nurses to protect patients from risks of unnecessary treatment and promote recovery. Impact: This study makes explicit nurses' essential safety role in recognising and responding to improvement in patients' clinical states. Healthcare policy and education must reflect the equal importance of assessment for and management of deterioration and improvement to ensure patients are protected and provided with safe care. [ABSTRACT FROM AUTHOR]

We sought to create an Italian version of Mishel's Uncertainty in Illness Scale, dedicated to people undergoing conservative rehabilitation for urinary incontinence, for studying uncertainty as a determinant of therapeutic adherence. Urinary incontinence has a high prevalence worldwide, ranging from 25% to 45%. Incontinence is often treatable with conservative interventions but demands a long and intensive commitment from the patient. Results are not immediate, and relapses are possible. These patients can experience uncertainty and difficulty complying with rehabilitation programs, hence the importance of the therapeutic relationship with a healthcare professional. Mishel's theory of uncertainty can be used to measure uncertainty and the effects of such a relationship, but no instrument currently exists for this purpose. Prospective observational study enrolling all male and female adult patients admitted to a nurse‐led outpatient pelvic clinic for non‐neurogenic urinary incontinence, excluding puerpera. A scale named MUIS‐PF (pelvic floor) was created, based on previous versions of Mishel's scale, and administered during the first consultation and at the end of the rehabilitation program. Internal consistency was assessed, and exploratory factor analysis was conducted. A total of 109 patients enrolled (54 M, 55 F) aged 64 ± 5 years, medial initial leakage 245 grams/day, IQR [90; 370]. Seventy‐nine percent obtained continence; there were no dropouts during the study. Internal consistency of the MUIS‐PF was high (93%), and structure analysis yielded a clear separation of the factors. Patient uncertainty decreased significantly at the end of the program compared to the first consultation (p < 0.001). The MUIS‐PF is valid and reliable. Utilizing the correct approach, the nurse could significantly reduce the uncertainty of persons with incontinence by listening, giving clear information and searching for the best solution for their continence issues. [ABSTRACT FROM AUTHOR]

Background: Risk score calculators are a widely developed tool to support clinicians in identifying and managing risk for certain diseases. However, little is known about physicians' applied experiences with risk score calculators and the role of risk score estimates in clinical decision making and patient communication. Methods: Physicians providing care in outpatient community-based clinical settings (N = 20) were recruited to participate in semi-structured individual interviews to assess their use of risk score calculators in practice. Two study team members conducted an inductive thematic analysis using a consensus-based coding approach. Results: Participants referenced at least 20 risk score calculators, the most common being the Atherosclerotic Cardiovascular Disease Risk Calculator. Ecological factors related to the clinical system (e.g., time), patient (e.g., receptivity), and physician (e.g., experience) influenced conditions and patterns of risk score calculator use. For example, compared with attending physicians, residents tended to use a greater variety of risk score calculators and with higher frequency. Risk score estimates were generally used in clinical decision making to improve or validate clinical judgment and in patient communication to serve as a motivational tool. Conclusions: The degree to which risk score estimates influenced physician decision making and whether and how these scores were communicated to patients varied, reflecting a nuanced role of risk score calculator use in clinical practice. The theory of planned behavior can help explain how attitudes, beliefs, and norms shape the use of risk score estimates in clinical decision making and patient communication. Additional research is needed to evaluate best practices in the use of risk score calculators and risk score estimates. Highlights: The risk score calculators and estimates that participants referenced in this study represented a range of conditions (e.g., heart disease, anxiety), levels of model complexity (e.g., probability calculations, scales of severity), and output formats (e.g., point estimates, risk intervals). Risk score calculators that are easily accessed, have simple inputs, and are trusted by physicians appear more likely to be used. Risk score estimates were generally used in clinical decision making to improve or validate clinical judgment and in patient communication to serve as a motivational tool. Risk score estimates helped participants manage the uncertainty and complexity of various clinical situations, yet consideration of the limitations of these estimates was relatively minimal. Developers of risk score calculators should consider the patient- (e.g., response to risk score estimates) and physician- (e.g., training status) related characteristics that influence risk score calculator use in addition to that of the clinical system. [ABSTRACT FROM AUTHOR]

Abstract Background Clinical decision-making (CDM) refers to physicians’ ability to gather, evaluate, and interpret relevant diagnostic information. An integral component of CDM is the medical history conversation, traditionally practiced on real or simulated patients. In this study, we explored the potential of using Large Language Models (LLM) to simulate patient-doctor interactions and provide structured feedback. Methods We developed AI prompts to simulate patients with different symptoms, engaging in realistic medical history conversations. In our double-blind randomized design, the control group participated in simulated medical history conversations with AI patients (control group), while the intervention group, in addition to simulated conversations, also received AI-generated feedback on their performances (feedback group). We examined the influence of feedback based on their CDM performance, which was evaluated by two raters (ICC = 0.924) using the Clinical Reasoning Indicator – History Taking Inventory (CRI-HTI). The data was analyzed using an ANOVA for repeated measures. Results Our final sample included 21 medical students (age mean = 22.10 years, semestermean = 4, 14 females). At baseline, the feedback group (mean = 3.28 ± 0.09 [standard deviation]) and the control group (3.21 ± 0.08) achieved similar CRI-HTI scores, indicating successful randomization. After only four training sessions, the feedback group (3.60 ± 0.13) outperformed the control group (3.02 ± 0.12), F (1,18) = 4.44, p = .049 with a strong effect size, partial η 2 = 0.198. Specifically, the feedback group showed improvements in the subdomains of CDM of creating context (p = .046) and securing information (p = .018), while their ability to focus questions did not improve significantly (p = .265). Conclusion The results suggest that AI-simulated medical history conversations can support CDM training, especially when combined with structured feedback. Such training format may serve as a cost-effective supplement to existing training methods, better preparing students for real medical history conversations.

INTRODUCTION: In the Investigating the Impact of Alzheimers Disease Diagnostics in British Columbia (IMPACT-AD BC) study, we aimed to understand how Alzheimers disease (AD) cerebrospinal fluid (CSF) biomarker testing-used in medical care-impacted medical decision-making (medical utility), personal decision-making (personal utility), and health system economics. METHODS: The study was designed as an observational, longitudinal cohort study. A total of 149 patients were enrolled between February 2019 and July 2021. Patients referred to memory clinics were approached to participate if their dementia specialist ordered AD CSF biomarker testing as part of their routine medical care, and the clinical scenario met the appropriate use criteria for lumbar puncture and AD CSF biomarker testing. For the medical utility pillar, detailed clinical management plans were collected via physician questionnaires pre- and post-biomarker disclosure. RESULTS: Patients with completed management questionnaires (n = 142) had a median age of 64 (interquartile range: 59-69) years, 48% were female, and 60% had CSF biomarker profiles on the AD continuum. Clinical management changed in 89.4% of cases. AD biomarker testing was associated with decreased need for other diagnostic procedures, including brain imaging (-52.0%) and detailed neuropsychological assessments (-63.2%), increased referrals and counseling (57.0%), and guided AD-related drug prescriptions (+88.4% and -50.0% in biomarker-positive and -negative cases, respectively). DISCUSSION: AD biomarker testing was associated with significant and positive changes in clinical management, including decreased health care resource use, therapy optimization, and increased patient and family member counseling. While certain changes in management were linked to the AD biomarker profile (e.g., referral to clinical trials), the majority of changes were independent of baseline clinical presentation and level of cognitive impairment, demonstrating a broad value for AD biomarker testing in individuals meeting the appropriate use criteria for testing.

Abstract Background Serious pathology masking as musculoskeletal conditions is rare, still it is pertinent that physiotherapists can recognise it. This ability has been investigated internationally, however the decision-making skills of registered Austrian physiotherapists has not been examined. The aim of this study was to assess the ability of registered Austrian physiotherapists to make accurate keep-refer decisions based on clinical vignettes. Methods In this national survey registered Austrian (self-)employed physiotherapists were recruited and completed 12 clinical vignettes. Correctly answered vignettes were listed as percentages. Results 479 physiotherapists participated in the study. The response rate of the self-employed physiotherapists was 8.0%. On average participants classified 70.5% of the musculoskeletal cases, 79.4% of the non-critical medical cases, and 53.3% of the critical medical cases correctly. Conclusion This study suggests that, despite the limitations of using written clinical vignettes, registered Austrian physiotherapists welcome additional training to improve their skills in identifying serious pathology. Targeted training and educational programs including new and more detailed educational clinical vignettes relevant for non-direct access countries are needed to enhance physiotherapists’ diagnostic skills and decision-making processes.

Abstract Background Anti-angiogenic, VEGF inhibitors (VEGFi) increase progression-free survival (PFS) and, in some cases, overall survival in many solid tumours. However, their use has been compromised by a lack of informative biomarkers. We have shown that plasma Tie2 is the first tumour vascular response biomarker for VEGFi in ovarian, colorectal and gall bladder cancer: If plasma Tie2 concentrations do not change after 9 weeks of treatment with a VEGFi, the patient does not benefit, whereas a confirmed reduction of at least 10% plasma Tie2 defines a vascular response with a hazard ratio (HR) for PFS of 0.56. The aim of the VALTIVE1 study is to validate the utility of plasma Tie2 as a vascular response biomarker and to optimise the Tie2-definition of vascular response so that the subsequent randomised discontinuation VALTIVE2 study can be powered optimally. Methods VALTIVE1 is a multi-centre, single arm, non-interventional biomarker study, with a sample size of 205 participants (176 bevacizumab-treated participants + 29 participants receiving bevacizumab and olaparib/PARPi), who are 16 years or older, have FIGO stage IIIc/IV ovarian cancer on treatment with first-line platinum-based chemotherapy and bevacizumab. Their blood plasma samples will be collected before, during, and after treatment and the concentration of Tie2 will be determined. The primary objective is to define the PFS difference between Tie2-defined vascular responders and Tie2-defined vascular non-responders in patients receiving bevacizumab for high-risk Ovarian Cancer. Secondary objectives include defining the relationship between Tie2-defined vascular progression and disease progression assessed according to RECIST 1.1 criteria and assessing the impact of PARPi on the plasma concentration of Tie2 and, therefore, the decision-making utility of Tie2 as a vascular response biomarker for bevacizumab during combined bevacizumab-PARPi maintenance. Discussion There is an urgent need to establish a test that tells patients and their doctors when VEGFi are working and when they stop working. The data generated from this study will be used to design a second trial aiming to prove conclusively the value of the Tie2 test. Trial registration ClinicalTrials.gov identifier: NCT04523116. Registered on 21 Aug 2020.

Abstract Background Learning policies for decision-making, such as recommending treatments in clinical settings, is important for enhancing clinical decision-support systems. However, the challenge lies in accurately evaluating and optimizing these policies for maximum efficacy. This paper addresses this gap by focusing on two key aspects of policy learning: evaluation and optimization. Method We develop counterfactual policy learning algorithms for practical clinical applications to suggest viable treatment for patients. We first design a bootstrap method for counterfactual assessment and enhancement of policies, aiming to diminish uncertainty in clinical decisions. Building on this, we introduce an innovative adversarial learning algorithm, inspired by bootstrap principles, to further advance policy optimization. Results The efficacy of our algorithms was validated using both semi-synthetic and real-world clinical datasets. Our method outperforms baseline algorithms, reducing the variance in policy evaluation by 30% and the error rate by 25%. In policy optimization, it enhances the reward by 1% to 3%, highlighting the practical value of our approach in clinical decision-making. Conclusion This study demonstrates the effectiveness of combining bootstrap and adversarial learning techniques in policy learning for clinical decision support. It not only enhances the accuracy and reliability of policy evaluation and optimization but also paves avenues for leveraging advanced counterfactual machine learning in healthcare.

Jing Yu,1 Yike Zhang,1 Zhixiong Ye,1 Kun Tang,1 Yiming Ma,1 Linlin Fu,1 Tongtong Cui,1 Hening Kang,2 Yadong Yuan,1 Wensen Pan1 1Second Department of Respiratory and Critical Care Medicine, The Second Hospital of Hebei Medical University, Shijiazhuang, People’s Republic of China; 2College of Basic Medicine, Hebei Medical University, Shijiazhuang, People’s Republic of ChinaCorrespondence: Wensen Pan, Email 27400010@hebmu.edu.cnBackground: Despite ongoing interventions, SARS-CoV-2 continues to cause significant global morbidity and mortality. Early diagnosis and intervention are crucial for effective clinical management. However, prognostic features based on transcriptional data have shown limited effectiveness, highlighting the need for more precise biomarkers to improve COVID-19 treatment outcomes.Methods: We retrospectively analyzed 149 clinical features from 189 COVID-19 patients, identifying prognostic features via univariate Cox regression. The cohort was split into training and validation sets, and 77 prognostic models were developed using seven machine learning algorithms. Among these, the least absolute shrinkage and selection operator (Lasso) method was employed to refine the selection of prognostic variables by ten-fold cross-validation strategy, which were then integrated with random survival forests (RSF) to build a robust COVID-19-related prognostic model (CRM). Model accuracy was evaluated across training, validation, and entire cohorts. The diagnostic relevance of interleukin-10 (IL-10) was confirmed in bulk transcriptional data and validated at the single-cell level, where we also examined changes in cellular communication between mononuclear cells with differing IL-10 expression and other immune cells.Results: Univariate Cox regression identified 43 prognostic features. Among the 77 machine learning models, the combination of Lasso and RSF produced the most robust CRM. This model consistently performed well across training, validation, and entire cohorts. IL-10 emerged as a key prognostic feature within the CRM, validated by single-cell transcriptional data. Transcriptome analysis confirmed the stable diagnostic value of IL-10, with mononuclear cells identified as the primary IL-10 source. Moreover, differential IL-10 expression in these cells was linked to altered cellular communication in the COVID-19 immune microenvironment.Conclusion: The CRM provides accurate prognostic predictions for COVID-19 patients. Additionally, the study underscores the importance of early IL-10 level testing upon hospital admission, which could inform therapeutic strategies.Keywords: SARS-CoV-2, machine learning, IL-10, therapeutic guidance, clinical decision making

Introduction: The deep inferior epigastric artery perforator (DIEP) free flap is the gold-standard for breast reconstruction but is technically demanding, resource intensive and time-consuming, making it a daunting task for the junior surgeon. Aim: To report the lessons learnt from the experience of a single surgeon performing their first 150 DIEP reconstructions as a guide for junior surgeons. Methods: Data regarding patient demographics and surgical outcomes from April 2021 to October 2022 were collected retrospectively from medical records. Surgical outcomes were analysed using Microsoft Excel. Results: Over 17 months, 150 flaps were completed in 97 patients by the senior author (BS). Operative duration was negatively correlated with case number for unilateral DIEPs (r = -0.73, p < 0.05) and for bilateral DIEPs (r = -0.67, p = 0.14). Raise time and ischaemic time were also negatively correlated with case number (r = -0.82, p < 0.05 and r = -0.79, p < 0.05, respectively). There were 10 complications and no flap losses. Discussion: The data demonstrate an expected improvement in surgical efficiency with increased experience. We describe the key factors contributing to efficiency in our series, such as preoperative CT angiography, surgical markings prior to the day of surgery, a two-team approach with three diathermy sets, flap raise using monopolar diathermy, preference towards choosing a single dominant perforator and early commitment to perforator choice. This case series acts as a guide for the junior plastic surgeon in achieving safe, aesthetic and efficient results when completing free DIEP flap breast reconstructions.

Background: Serious pathology masking as musculoskeletal conditions is rare, still it is pertinent that physiotherapists can recognise it. This ability has been investigated internationally, however the decision-making skills of registered Austrian physiotherapists has not been examined. The aim of this study was to assess the ability of registered Austrian physiotherapists to make accurate keep-refer decisions based on clinical vignettes. Methods: In this national survey registered Austrian (self-)employed physiotherapists were recruited and completed 12 clinical vignettes. Correctly answered vignettes were listed as percentages. Results: 479 physiotherapists participated in the study. The response rate of the self-employed physiotherapists was 8.0%. On average participants classified 70.5% of the musculoskeletal cases, 79.4% of the non-critical medical cases, and 53.3% of the critical medical cases correctly. Conclusion: This study suggests that, despite the limitations of using written clinical vignettes, registered Austrian physiotherapists welcome additional training to improve their skills in identifying serious pathology. Targeted training and educational programs including new and more detailed educational clinical vignettes relevant for non-direct access countries are needed to enhance physiotherapists' diagnostic skills and decision-making processes. [ABSTRACT FROM AUTHOR]

Background: Anti-angiogenic, VEGF inhibitors (VEGFi) increase progression-free survival (PFS) and, in some cases, overall survival in many solid tumours. However, their use has been compromised by a lack of informative biomarkers. We have shown that plasma Tie2 is the first tumour vascular response biomarker for VEGFi in ovarian, colorectal and gall bladder cancer: If plasma Tie2 concentrations do not change after 9 weeks of treatment with a VEGFi, the patient does not benefit, whereas a confirmed reduction of at least 10% plasma Tie2 defines a vascular response with a hazard ratio (HR) for PFS of 0.56. The aim of the VALTIVE1 study is to validate the utility of plasma Tie2 as a vascular response biomarker and to optimise the Tie2-definition of vascular response so that the subsequent randomised discontinuation VALTIVE2 study can be powered optimally. Methods: VALTIVE1 is a multi-centre, single arm, non-interventional biomarker study, with a sample size of 205 participants (176 bevacizumab-treated participants + 29 participants receiving bevacizumab and olaparib/PARPi), who are 16 years or older, have FIGO stage IIIc/IV ovarian cancer on treatment with first-line platinum-based chemotherapy and bevacizumab. Their blood plasma samples will be collected before, during, and after treatment and the concentration of Tie2 will be determined. The primary objective is to define the PFS difference between Tie2-defined vascular responders and Tie2-defined vascular non-responders in patients receiving bevacizumab for high-risk Ovarian Cancer. Secondary objectives include defining the relationship between Tie2-defined vascular progression and disease progression assessed according to RECIST 1.1 criteria and assessing the impact of PARPi on the plasma concentration of Tie2 and, therefore, the decision-making utility of Tie2 as a vascular response biomarker for bevacizumab during combined bevacizumab-PARPi maintenance. Discussion: There is an urgent need to establish a test that tells patients and their doctors when VEGFi are working and when they stop working. The data generated from this study will be used to design a second trial aiming to prove conclusively the value of the Tie2 test. Trial registration: ClinicalTrials.gov identifier: NCT04523116. Registered on 21 Aug 2020. [ABSTRACT FROM AUTHOR]

Background Objective Methods Results Conclusions The MDS‐UPDRS Parts IB and II are self‐reported items providing a direct patient voice to the experiences of PD.To determine the most sensitive combination of MDS‐UPDRS Parts IB and II items that accurately predicted the clinically relevant target of dopaminergic therapy initiation.Utilizing a longitudinal cohort of de novo non‐treated PD patients, we applied item response theory (IRT) and survival analysis to assess the relationship between baseline patient‐reported symptoms and the later initiation of dopaminergic therapy. The 20 MDS‐UPDRS Parts IB and II items were analyzed for their relationship to PD severity (discrimination) and the amount of information they provided in this determination (information). These parameters were used to develop models of predictive accuracy for initiation of dopaminergic therapy.A six‐item version showed a significantly higher C‐index as compared to the full 20 item model (P = 0.001). This shortened version of the MDS‐UPDRS contained only Part II items and provided a predictive accuracy for initiation of dopaminergic therapy better than the total combined scale score or any other combination.A six‐item “Baseline Outcome Voice” version of patient‐reported MDS‐UPDRS items significantly increases the sensitivity of predicting the key future clinical outcome of starting dopaminergic treatment in early PD. This study also demonstrates how IRT modeling can provide information useful to refining existing measures to identify the most sensitive combination of items honoring the voice of the patient in determining key clinically pertinent decisions. Further research is needed to validate these findings in underrepresented populations. [ABSTRACT FROM AUTHOR]

Introduction: The clinicopathological characteristics and prognosis of placental site trophoblastic tumor (PSTT) and epithelioid trophoblastic tumor (ETT) have not been well summarized. Consequently, we conducted the largest to date series of samples of both types and employed machine learning (ML) to assess treatment effectiveness and develop accurate prognostic models for patients with GTN. Gestational choriocarcinoma (GCC) was used as the control group to show the clinical features of PTSS and ETT. Methods: The Surveillance, Epidemiology, and End Results (SEER) database provided the data used for this study's analysis. To identify the prognostic variables, we conducted Cox regression analysis and constructed prognostic models using five ML algorithms to predict the 5-year survival. A validation method incorporating the area under the curve (AUC) of the receiver operating characteristic (ROC) curve was used to validate the accuracy and reliability of the ML models. We also investigated the role of multiple therapeutic options using the Kaplan-Meier survival analysis. Results: The study population comprised 725 patients. Among them, 139 patients had ETT, 107 had PSTT, and 479 had GCC. There were no significant differences in survival between the different tumor groups. Multivariate Cox regression analysis revealed that metastasis was a significant prognostic factor for GCC, while older age and radiotherapy were significant prognostic factors for PTSS and ETT. ML models revealed that the Gradient Boosting classifier accurately predicted the outcomes, followed by the random forest classifier, K-Nearest Neighbors, Logistic Regression, and multilayer perceptron models. The most significant contributing factors were tumor size, year of diagnosis, age, and race. Discussion: Our study provides a method for treatment and prognostic assessment of patients with GTN. The ML we developed can be used as a convenient individualized tool to facilitate clinical decision making. [ABSTRACT FROM AUTHOR]

Fractional flow reserve (FFR) is regarded as the gold standard for assessing the functional significance of coronary artery lesions. However, its utilization in clinical practice remains limited. This study aims to determine whether FFR results can influence treatment decisions for coronary artery disease compared to visual assessments of angiographic images. We conducted a retrospective study involving 63 patients diagnosed with either chronic coronary syndrome (n = 39, 61.9%) or acute coronary syndrome (n = 24, 38.1%) who underwent an FFR assessment. Three experienced interventional cardiologists (>300 PCI procedures/year) reevaluated 105 ambiguous coronary lesions in these patients, blinded to the FFR results. The objective was to assess lesion significance and determine the treatment strategy based on a visual angiographic evaluation. The three operators reached concordant agreement (≥two operators) to perform PCI in 60 (57.1%) of the evaluated lesions based on the angiographic assessment. Of these, nine lesions (15%) were deemed functionally non-significant by FFR (FFR > 0.80). Conversely, they agreed to defer PCI in 45 (42.9%) lesions, but 4 lesions (8.9%) were found to be functionally significant (FFR ≤ 0.80) and required a re-evaluation for PCI. Visual-guided decision making by interventional cardiologists shows variability and does not always align with the functional significance of coronary lesions as determined by FFR. Incorporating FFR into routine decision making could enhance treatment accuracy and patient outcomes. [ABSTRACT FROM AUTHOR]

Despite efforts to incorporate evidence-based practice into mental health settings, there has been inconsistent success doing so. Several barriers to the implementation of evidence-based practice have been identified, including limitations in time and statistical training. One promising approach to further bridge the science-practice gap is the greater use of meta-analytic studies to clarify the magnitude of treatment effects and to understand components of treatments that are more effective within specific populations. Although several articles have been published to help mental health trainees and providers understand and interpret meta-analyses, they may not be sufficient without extensive training in statistical techniques. The purpose of this article is to provide and explain the use of the Meta-Analysis Application Worksheet as a means by which mental health providers at all professional levels can use meta-analytic findings to make clinical judgments regarding treatment approaches in the context of ongoing or new cases. A case example is provided to illustrate the use of the worksheet in clinical decision making. Public Significance Statement: Education and experience with meta-analyses may be lacking among mental health professionals. The article describes the creation and use of an educational worksheet to help clinicians apply the findings from meta-analyses to their practice to further increase the use of evidence-based treatments. [ABSTRACT FROM AUTHOR]

Purpose: Recently introduced hybrid 2-[18 F]-fluoro-2-deoxy-D-glucose (18 F-FDG) Positron Emission Tomography (PET) combined with Magnetic Resonance Imaging (MRI) may aid in proper diagnosis and staging of perihilar cholangiocarcinoma (pCCA). The aim of this study is to assess the effect of 18 F-FDG PET/MRI on diagnosis and clinical decision making in the pre-operative work up of pCCA. Methods: In this single-centre pilot study patients with presumed resectable pCCA underwent state-of-the-art 18 F-FDG hybrid PET/MRI using digital silicone photomultiplier detectors integrated within a 3-Tesla bore. Data were collected on several baseline and imaging characteristics. The primary outcome measure was the added diagnostic information and the effect on clinical decision making. Secondary aim was to correlate quantitative PET signal intensity to patient- and tumour characteristics. High and low SUVmax subgroups related to the mean value were made. Significance of lesion- and patient characteristics with the high and low SUVmax subgroups, as well as TLR and TBR, was evaluated with Fisher's exact test or Mann-Whitney-U test. Results: In total 14 patients were included (mean age 62.4 years, 64% male). Final diagnosis was pCCA in 10 patients (71.4%), follicular lymphoma in one patient (7.1%) and benign disease in the remaining three patients. FDG-PET/MRI added valuable diagnostic information in six (43%) patients and affected clinical decision making in two of these patients (14%) by increasing confidence for malignancy which lead to the decision for surgery on short term. High SUVmax values were seen in half of cases with pCCA and half of cases with non-cancerous lesions. In addition, high SUVmax values were directly associated with primary sclerosing cholangitis when present (p = 0.03). Conclusion: Simultaneous 18 F-FDG-PET/MRI added diagnostic information in six of fourteen patients and influenced clinical decision making in two patients (14%) with presumed resectable pCCA. [ABSTRACT FROM AUTHOR]

Background: Learning policies for decision-making, such as recommending treatments in clinical settings, is important for enhancing clinical decision-support systems. However, the challenge lies in accurately evaluating and optimizing these policies for maximum efficacy. This paper addresses this gap by focusing on two key aspects of policy learning: evaluation and optimization. Method: We develop counterfactual policy learning algorithms for practical clinical applications to suggest viable treatment for patients. We first design a bootstrap method for counterfactual assessment and enhancement of policies, aiming to diminish uncertainty in clinical decisions. Building on this, we introduce an innovative adversarial learning algorithm, inspired by bootstrap principles, to further advance policy optimization. Results: The efficacy of our algorithms was validated using both semi-synthetic and real-world clinical datasets. Our method outperforms baseline algorithms, reducing the variance in policy evaluation by 30% and the error rate by 25%. In policy optimization, it enhances the reward by 1% to 3%, highlighting the practical value of our approach in clinical decision-making. Conclusion: This study demonstrates the effectiveness of combining bootstrap and adversarial learning techniques in policy learning for clinical decision support. It not only enhances the accuracy and reliability of policy evaluation and optimization but also paves avenues for leveraging advanced counterfactual machine learning in healthcare. [ABSTRACT FROM AUTHOR]

Background: Living donor kidney transplantation is the optimal treatment for end‐stage kidney disease; however, few living donor candidates (LDCs) who begin evaluation actually donate. While some LDCs are deemed medically ineligible, others discontinue for potentially modifiable reasons. Methods: At five transplant centers, we conducted a prospective cohort study measuring LDCs' clinical and psychosocial characteristics, educational preparation, readiness to donate, and social determinants of health. We followed LDCs for 12 months after evaluation to determine whether they donated a kidney, opted to discontinue, had modifiable reasons for discontinuing, were medically ineligible, or had other recipient‐related reasons for discontinuing. Results: Among 2184 LDCs, 18.6% donated, 38.2% opted to or had modifiable reasons for discontinuing, and 43.2% were deemed ineligible due to medical or recipient‐related reasons. Multivariable analyses comparing successful LDCs with those who did not complete donation for modifiable reasons (N = 1241) found that LDCs who discussed donation with the recipient before evaluation (OR, 2.31; 95% CI, 1.54–3.46), had completed high school (OR, 2.01; 95% CI, 1.21–3.35), or were a "close relation" to their recipient (OR, 1.89; 95% CI, 1.33–2.69) were more likely to donate. Conversely, LDCs who reported religion as important (OR, 0.55; 95% CI, 0.38–0.80), were Non‐White (OR, 0.70; 95% CI, 0.49–1.00), or had overall higher anxiety scores (OR, 0.92; 95% CI, 0.86–0.99) were less likely to donate. Conclusion: With fewer than a fifth of LDCs donating, developing programs to provide greater emotional support and facilitate open discussions between LDCs and recipients earlier may increase living donation rates. [ABSTRACT FROM AUTHOR]

Background: Clinicians make clinical decisions using the dual process theory. The dual process theory comprises two approaches, System 1, based on heuristics, and System 2, involving an analytical and effortful thought process. However, there are inherent limitations to the dual process theory, such as relying on inaccurate memory or misinterpreting cues leading to inappropriate clinical management. As a result, clinicians may utilize mental shortcuts, termed heuristics, and be susceptible to clinical errors and biases that may lead to flawed decision making and diagnosis. Methods: This case series describes four clinical cases whereby the clinicians use distinct strategies to assess and manage complex clinical presentations. Discussion: Through the use of self-reflection and acknowledging diagnostic uncertainty, the clinicians were able to reduce common cognitive biases and provide effective and timely patient care. We discuss strategies that clinicians can implement in their daily practice to improve clinical decision-making processes and deliver quality care. [ABSTRACT FROM AUTHOR]

With the incorporation of evidence-based practice (EBP) during patient care as a curricular content standard, professional programs must prepare athletic training students (ATSs) for the application of EBP during the delivery of patient care. We aimed to examine ATSs' implementation of professional behaviors associated with EBP during patient encounters (PEs). Through a multisite panel design, we tracked numerous factors associated with PEs experienced by 363 ATSs of 12 professional athletic training programs. Generalized estimating equation models were used to analyze the likelihood that students included EBP behaviors during 30,522 PEs. Clinical site type (p <.001), student role (p <.001), and encounter length (p <.001) were associated with all three EBP professional behaviors while clinical site type (p <.001) was also associated with whether the ATS asked a question of a clinician, including their preceptor. Program administrators seeking to promote the greatest opportunities for ATSs to implement EBP during patient care should seek clinic-based or other nonacademic site types that promote longer PEs in which students can perform or assist their preceptor. [ABSTRACT FROM AUTHOR]

Some studies have reported that community pharmacies in developing countries, including Indonesia, provided sub-optimal advice when handling patient's self-medication request for cough. The reasons behind such advice, therefore, need to be investigated. To describe Indonesian pharmacists' clinical decision making when handling self-medication cases for a cough. An open-ended questionnaire consisting of two cough clinical vignettes (case 1: cough due to asthma worsening and case 2: cough as a symptom of common cold) were developed. Pharmacists were interviewed to provide recommendations and reasons for their recommendations for these scenarios. Content analysis was used to analyse participants' statements for the two scenarios. The number of participants who provided appropriate recommendations and reasons were then counted. A total of 245 community pharmacists participated in the study. In the case of cough due to asthma worsening, recommending a product because the product was indicated to help with the symptoms was the most common recommendation and stated reason (40%). Appropriate recommendation (direct medical referral) with appropriate reasoning (indicating warning symptoms and/or making a symptom diagnosis) was provided by 25% participants. In the case of cough as a symptom of common cold, recommending products to help with the symptoms was also the most common recommendation and stated reason (53%). Appropriate recommendations (recommending product) with appropriate reasoning (providing product to treat the symptoms and/or indicating no warning symptoms and/or making a symptom diagnosis) was provided by 81% participants. The ability of Indonesian community pharmacists to provide appropriate recommendations for cough self-medication requests is dependent on whether triage is required. The inability of most community pharmacists to differentiate between major and minor conditions may lead to serious health implications for patients and therefore educational interventions should be undertaken to improve community pharmacists' differential diagnostic skills for triage. [ABSTRACT FROM AUTHOR]

Precision medicine has emerged as a transformative approach aimed at tailoring treatment to individual patients, moving away from the traditional one-size-fits-all model. However, Clinical decision support systems encounter challenges, particularly in terms of data aspects. In response, our study proposes a data-driven framework rooted in Simon's decision-making model. This framework leverages advanced technologies such as artificial intelligence and data analytics to enhance clinical decision-making in precision medicine. By addressing limitations and integrating AI and analytics, our study contributes to the advancement of optimal clinical decision-making practices in precision healthcare. [ABSTRACT FROM AUTHOR]

Introduction: Emotional intelligence is a crucial factor in managing stress and maintaining overall well-being. Emotions are essential for clinical care quality, as they drive clinical decision making. Objective: The purpose of this study was to assess the relationship between emotional intelligence and clinical decision making among fourth-year nursing students. Methods: The study was a cross-sectional study done on a convenience sample of 225 fourth-year nursing students. Data collection was performed by the "Schutte Self Report Emotional Intelligence Test" (SSEIT) and the "clinical decision making scale". Results: The analysis revealed that the emotional intelligence mean was 151.3 ± 1.9 (ranging from 33 to 165), which is high. The analysis indicated that the clinical decision making of the participants was high (177.1.3 ± 9.8) (ranging from 40 to 200). Also, the analysis showed that there was a strong positive relationship between nursing students' emotional intelligence and clinical decision making (r =.70, p = 0.001). Furthermore, the findings revealed that emotional intelligence was a predictor of clinical decision making. Conclusion: The emotional intelligence and clinical decision making among fourth-year nursing students were high. Also, clinical decision making was found to have strong positive relationship with emotional intelligence. [ABSTRACT FROM AUTHOR]

Introduction: The primary purpose of this study was to examine the perceptions and utilization of spinal thrust manipulation (STM) techniques of physical therapists who treat patients with low back pain (LBP) in the State of Minnesota. A secondary purpose was to investigate differences between physical therapists who perform STM and those who do not. Methods: A cross-sectional design was utilized through the completion of an electronic survey. 74 respondents completed the survey. Descriptive measures were recorded as frequencies for categorical data or mean ± standard deviation for continuous data. For between-group comparisons, chi-square analyses were used for categorical items of nominal or ordinal data and t-tests were utilized for continuous data. The alpha level was set at p < 0.05. Result: 60.2% of respondents reported using STM when treating patients with LBP. 69.9% of respondents utilize a classification system. 76.7% of individuals answered correctly regarding the Minnesota State practice act. Of those who use STM, 81.8% utilize a Clinical Prediction Rule. Respondents who use STM were more likely to have a specialist certification (chi-square = 6.471, p = 0.011) and to have completed continuing education courses on manual therapy (chi-square = 4.736, p = 0.030). Discussion/Conclusions: Physical therapists who perform STM are more likely to have a better understanding of their state practice act, be board certified, and have completed continuing education in manual therapy. KEY POINTS: This research builds upon the understanding of clinical practice patterns of physical therapists, specific to utilization and clinical decision making for manual therapy treatment interventions for LBP. Adherence to treatment recommendations for LBP could benefit from further educational within the profession of physical therapy, mainly continuing education, promotion of clinical residency training, and/or attainment of specialist board certification. Physical therapists who perform STM are more likely to have a better understanding of their state physical therapy practice act, have further training as evidenced by board certification, and have pursued continuing education in the realm of manual therapy or manipulation techniques. [ABSTRACT FROM AUTHOR]

BACKGROUND: Mobility loss is common in hospitalized older adults, and resources to prevent mobility impairment are finite. Our goal was to use routinely collected data to develop a risk assessment tool that identifies individuals at risk of losing the ability to walk during hospitalization on the first hospital day. Second, we determined if the tool could inform the use of mobility-preserving interventions. METHODS: We included patients admitted to a general medical service, aged ≥65 years, who walked occasionally or frequently on admission (Braden Scale Activity subset > = 3). Patients were considered to have a new mobility impairment if, at discharge, their ability to walk was severely limited or nonexistent or they were confined to bed (Braden Scale Activity subset

Introduction Spontaneous pneumothorax (SP) affects both young, otherwise healthy individuals and older persons with known underlying pulmonary disease. Initial management possibilities are evolving and range from observation to chest tube insertion. SP guidelines suggest an individualized approach based on multiple factors such as symptoms, size of pneumothorax, comorbidity and patient preference.Aim With this Danish national survey we aimed to map organization of care including involved specialties, treatment choice, training, and follow-up plans to identify aspects, and optimization of spontaneous pneumothorax management.Method A survey developed by the national interest group for pleural medicine was sent to all departments of emergency medicine, thoracic surgery, respiratory medicine, and to relevant departments of abdominal or orthopaedic surgery.Results The response rate was 75 % (47 of 65). Overall, 21% of responding departments had no guideline for SP management, which was provided by multiple specialties with marked heterogeneity in choice of treatment including tube size, management during admission, and referral procedure to follow-up. Few departments required procedure training, and nearly all of the responders called for improvements in management of pneumothorax.Conclusion This survey suggests that SP management and care is delivered heterogeneously across Danish hospitals with marked difference between respiratory physicians, emergency physicians, general surgeons and thoracic surgeons. It is therefore likely that management is sub-optimal. There is a need for a common Danish SP guideline to ensure optimal treatment across involved specialties.

Neurofibromatosis type 1 (NF1), the most common tumor predisposition syndrome, occurs when NF1 gene variants result in loss of neurofibromin, a negative regulator of RAS activity. Plexiform neurofibromas (PN) are peripheral nerve sheath tumors that develop in patients with NF1 and are associated with substantial morbidity and for which, until recently, the only treatment was surgical resection. However, surgery carries several risks and a proportion of PN are considered inoperable. Understanding the genetic underpinnings of PN led to the investigation of targeted therapies as medical treatment options, and the MEK1/2 inhibitor selumetinib has shown promising efficacy in pediatric patients with NF1 and symptomatic, inoperable PN. In a phase I/II trial, most children (approximately 70%) achieved reduction in tumor volume accompanied by improvements in patient-reported outcomes (decreased tumor-related pain and improvements in quality of life, strength, and range of motion). Selumetinib is currently the only licensed medical therapy indicated for use in pediatric patients with symptomatic, inoperable NF1-PN, with approval based on the results of this pivotal clinical study. Several other MEK inhibitors (binimetinib, mirdametinib, trametinib) and the tyrosine kinase inhibitor cabozantinib are also being investigated as medical therapies for NF1-PN. Careful consideration of multiple aspects of both disease and treatments is vital to reduce morbidity and improve outcomes in patients with this complex and heterogeneous disease, and clinicians should be fully aware of the risks and benefits of available treatments. There is no single treatment pathway for patients with NF1-PN; surgery, watchful waiting, and/or medical treatment are options. Treatment should be individualized based on recommendations from a multidisciplinary team, considering the size and location of PN, effects on adjacent tissues, and patient and family preferences. This review outlines the treatment strategies currently available for patients with NF1-PN and the evidence supporting the use of MEK inhibitors, and discusses key considerations in clinical decision-making.

Abstract Background The ability of a nurse to make effective clinical decisions is the most important factor that can affect the treatment quality. However, several factors can affect the ability of nursing and midwifery students to make effective clinical decisions. Objectives This study aims to identify the confounding factors that may affect the clinical decision making of nurses and thus patient outcomes after the COVID-19 pandemic in Jordan. Methods A descriptive cross-sectional design was employed in this study. An online self-administered questionnaire was distributed to 269 nursing and midwifery students selected through purposive sampling, 224 of whom completed the questionnaire. The valid and reliable nursing decision-making instrument, which consisted of 24 items, was employed to gather the data, and descriptive statistics and simple linear regression were employed for the data analysis. Data was collected from November to the end of December 2022. Results Among the respondents, 72.8% were female, and the average age was 20.79 years (SD = 1.44). The vast majority of the respondents (94.6%) was unmarried, and 74.1% were pursuing a nursing degree. The simple linear regression analysis showed that clinical decision making had a negative and significant relationship with social media usage of an average of 6 h a day (β=−0.085). Moreover, the male nursing students obtained lower clinical decision-making scores (β= −0.408) compared with the female nursing students. Conclusion Social media usage and gender have a considerable effect on the clinical decision making of the nursing and midwifery students. Therefore, the confounding factors that can affect the clinical decision making of nurses should be discussed further, and strategies to address such factors should be implemented.

Abstract Background In the hospital setting, frailty is a significant risk factor, but difficult to measure in clinical practice. We propose a reweighting of an existing diagnoses-based frailty score using routine data from a tertiary care teaching hospital in southern Germany. Methods The dataset includes patient characteristics such as sex, age, primary and secondary diagnoses and in-hospital mortality. Based on this information, we recalculate the existing Hospital Frailty Risk Score. The cohort includes patients aged ≥ 75 and was divided into a development cohort (admission year 2011 to 2013, N = 30,525) and a validation cohort (2014, N = 11,202). A limited external validation is also conducted in a second validation cohort containing inpatient cases aged ≥ 75 in 2022 throughout Germany (N = 491,251). In the development cohort, LASSO regression analysis was used to select the most relevant variables and to generate a reweighted Frailty Score for the German setting. Discrimination is assessed using the area under the receiver operating characteristic curve (AUC). Visualization of calibration curves and decision curve analysis were carried out. Applicability of the reweighted Frailty Score in a non-elderly population was assessed using logistic regression models. Results Reweighting of the Frailty Score included only 53 out of the 109 frailty-related diagnoses and resulted in substantially better discrimination than the initial weighting of the score (AUC = 0.89 vs. AUC = 0.80, p

BACKGROUND: This review aimed to identify variables influencing clinicians application of spinal manipulative therapy (SMT) for persistent spine pain after lumbar surgery (PSPS-2). We hypothesized markers of reduced clinical/surgical complexity would be associated with greater odds of applying SMT to the lumbar region, use of manual-thrust lumbar SMT, and SMT within 1-year post-surgery as primary outcomes; and chiropractors would have increased odds of using lumbar manual-thrust-SMT compared to other practitioners. METHODS: Per our published protocol, observational studies describing adults receiving SMT for PSPS-2 were included. PubMed, Web of Science, Scopus, OVID, PEDro, and Index to Chiropractic Literature were searched from inception to January 6, 2022. Individual patient data (IPD) were requested from contact authors when needed for selection criteria. Data extraction and a customized risk-of-bias rubric were completed in duplicate. Odds ratios (ORs) for primary outcomes were calculated using binary logistic regressions, with covariates including age, sex, symptom distribution, provider, motion segments, spinal implant, and surgery-to-SMT interval. RESULTS: 71 articles were included describing 103 patients (mean age 52 ± 15, 55% male). The most common surgeries were laminectomy (40%), fusion (34%), and discectomy (29%). Lumbar SMT was used in 85% of patients; and of these patients was non-manual-thrust in 59%, manual-thrust in 33%, and unclear in 8%. Clinicians were most often chiropractors (68%). SMT was used > 1-year post-surgery in 66% of cases. While no primary outcomes reached significance, non-reduced motion segments approached significance for predicting use of lumbar-manual-thrust SMT (OR 9.07 [0.97-84.64], P = 0.053). Chiropractors were significantly more likely to use lumbar-manual-thrust SMT (OR 32.26 [3.17-327.98], P = 0.003). A sensitivity analysis omitting high risk-of-bias cases (missing ≥ 25% IPD) revealed similar results. CONCLUSIONS: Clinicians using SMT for PSPS-2 most often apply non-manual-thrust SMT to the lumbar spine, while chiropractors are more likely to use lumbar-manual-thrust SMT relative to other providers. As non-manual-thrust SMT may be gentler, the proclivity towards this technique suggests providers are cautious when applying SMT after lumbar surgery. Unmeasured variables such as patient or clinician preferences, or limited sample size may have influenced our findings. Large observational studies and/or international surveys are needed for an improved understanding of SMT use for PSPS-2. Systematic review registration PROSPERO (CRD42021250039).

Background: Mental state deterioration poses significant challenges in healthcare, impacting patients and providers. Symptoms like confusion and agitation can lead to prolonged hospital stays, increased costs, and the use of restrictive interventions. Despite its prevalence, there's a lack of consensus on effective practices for managing mental state deterioration in acute hospital settings. To address this gap, a rapid response team model has been proposed as a potential intervention, aiming to provide early identification and targeted interventions. Methods: Based on realist evaluation steps, first, initial program theories are formulated to understand the logic behind the intervention. Second, literature synthesis identifies empirical evidence on contexts, mechanisms, and outcomes elements, refining initial theories. During the third step, data will be collected using qualitative methods such as field observations and interviews, as well as quantitative methods such as surveys of the staff, audits of electronic medical records, and analysis of incident records of mental state deterioration. Analysing this data informs configurations of contexts, mechanisms, and outcomes. In the fifth step, the configurations are synthesised, presenting refined, evidence-informed program theories. Conclusion: This study addresses the knowledge gap by evaluating the rapid response model's effectiveness in managing mental state deterioration in acute hospital settings. Realist principles guide the exploration of causal mechanisms and their interaction with specific implementation contexts. The objective is to identify what works, for whom, and under what circumstances, aiming to manage deterioration, reduce restrictive interventions, and enhance the experience for patients and staff by implementing a proactive model of care. The findings contribute to evidence-based approaches for managing mental state deterioration in hospital settings, informing policy and practice in this crucial area of healthcare. [ABSTRACT FROM AUTHOR]

Issue Addressed Methods Results Conclusions So What? The oral glucose tolerance test is the ‘gold standard’ for detecting gestational diabetes in Australian and International guidelines. Test completion in regional, rural and remote regions may be as low as 50%. We explored challenges and enablers for regional, rural and remote antenatal clinicians providing gestational diabetes screening to better understand low oral glucose tolerance test completion.We conducted a qualitative descriptive study using semi‐structured interviews. Participants eligible for the study were doctors or midwives providing antenatal care in regional, rural and remote Western Australia, between August 2019 and November 2020. Interviews were recorded digitally and transcribed into a Word document. We conducted a thematic analysis after initial categorisation and deduction of themes through workshops involving the research team.We found a diversity of viewpoints on oral glucose tolerance test reliability for detecting gestational diabetes. Themes that emerged were; good collaboration between antenatal clinicians is required for successful screening; screening occurs throughout pregnancy using various tests; clinicians make significant efforts to address barriers; clinicians prioritise therapeutic relationships.Effective universal screening for gestational diabetes in regional, rural and remote Western Australia is difficult and more complex in practice than guidelines imply. Detecting gestational diabetes requires creative solutions, early identification of at risk women and trust and collaboration between clinicians and women.Detection of gestational diabetes in regional, rural and remote Western Australia remains poorly completed. New strategies are required to adequately identify women at risk of adverse birth outcomes relating to hyperglycaemia in pregnancy. [ABSTRACT FROM AUTHOR]

Large language models (LLMs) have seen explosive growth, but their potential role in medical applications remains underexplored. Our study investigates the capability of LLMs to predict the most appropriate imaging study for specific clinical presentations in various subspecialty areas in radiology. Chat Generative Pretrained Transformer (ChatGPT), by OpenAI and Glass AI by Glass Health were tested on 1,075 clinical scenarios from 11 ACR expert panels to determine the most appropriate imaging study, benchmarked against the ACR Appropriateness Criteria. Two responses per clinical presentation were generated and averaged for the final clinical presentation score. Clinical presentation scores for each topic area were averaged as its final score. The average of the topic scores within a panel determined the final score of each panel. LLM responses were on a scale of 0 to 3. Partial scores were given for nonspecific answers. Pearson correlation coefficient (R-value) was calculated for each panel to determine a context-specific performance. Glass AI scored significantly higher than ChatGPT (2.32 ± 0.67 versus 2.08 ± 0.74, P =.002). Both LLMs performed the best in the Polytrauma, Breast, and Vascular panels, and performed the worst in the Neurologic, Musculoskeletal, and Cardiac panels. Glass AI outperformed ChatGPT in 10 of 11 panels, except Obstetrics and Gynecology. Maximum agreement was in the Pediatrics, Neurologic, and Thoracic panels, and the most disagreement occurred in the Vascular, Breast, and Urologic panels. LLMs can be used to predict imaging studies, with Glass AI's superior performance indicating the benefits of extra medical-text training. This supports the potential of LLMs in radiologic decision making. [Display omitted] [ABSTRACT FROM AUTHOR]

This Paper guides a review platform which allows to evaluate Artificial intelligence assisted diagnosis for blood cancer using machine learning. Advanced medical and technology-based research has fuelled the adoption of latest technologies for the sake of advancement in medical science application and overall improvement in detection, diagnosis, prevention and treatment of diseases. AI technology is being used widely in medicine, economy and daily life; in medicine, artificial intelligence is used for mainly treatment, diagnosis and prediction of disease prognosis. This review effectively highlights the wide-ranging applications of AI in medicine, with a specific focus on its contribution to treatment, diagnosis, prognosis and prediction. [ABSTRACT FROM AUTHOR]

Background: The ability of a nurse to make effective clinical decisions is the most important factor that can affect the treatment quality. However, several factors can affect the ability of nursing and midwifery students to make effective clinical decisions. Objectives: This study aims to identify the confounding factors that may affect the clinical decision making of nurses and thus patient outcomes after the COVID-19 pandemic in Jordan. Methods: A descriptive cross-sectional design was employed in this study. An online self-administered questionnaire was distributed to 269 nursing and midwifery students selected through purposive sampling, 224 of whom completed the questionnaire. The valid and reliable nursing decision-making instrument, which consisted of 24 items, was employed to gather the data, and descriptive statistics and simple linear regression were employed for the data analysis. Data was collected from November to the end of December 2022. Results: Among the respondents, 72.8% were female, and the average age was 20.79 years (SD = 1.44). The vast majority of the respondents (94.6%) was unmarried, and 74.1% were pursuing a nursing degree. The simple linear regression analysis showed that clinical decision making had a negative and significant relationship with social media usage of an average of 6 h a day (β=−0.085). Moreover, the male nursing students obtained lower clinical decision-making scores (β= −0.408) compared with the female nursing students. Conclusion: Social media usage and gender have a considerable effect on the clinical decision making of the nursing and midwifery students. Therefore, the confounding factors that can affect the clinical decision making of nurses should be discussed further, and strategies to address such factors should be implemented. [ABSTRACT FROM AUTHOR]