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Abstract Reports of Large Language Models (LLMs) passing board examinations have spurred medical enthusiasm for their clinical integration. Through a narrative review, we reflect upon the skill shifts necessary for clinicians to succeed in an LLM-enabled world, achieving benefits while minimizing risks. We suggest how medical education must evolve to prepare clinicians capable of navigating human-AI systems.

Our primary objective was to examine the impact of acquiescent responding on empathy measures. We selected the Affective and Cognitive Measure of Empathy (ACME) as the measure for this case study due to its composition--the affective dissonance scale consists solely of items that are semantically reversed relative to the empathy construct, while the resonance scale comprises mostly semantically positive items--which raises questions about whether the distinction between resonance and dissonance factors is authentic or merely an effect of acquiescence. Utilizing data from 2,489 Brazilian adults, we explored ACME's factor structure by testing various models, both with and without acquiescence control. We also examined the invariance of model parameters, including those for acquiescence, across self-reported sex and assessed the influence of acquiescence on the associations between ACME's factors and external measures. Our findings indicated that while acquiescence does affect ACME, the resonance and dissonance factors are not exclusively a result of this bias. Nevertheless, it was demonstrated that both the items and their associations with external measures are significantly impacted by acquiescence. These results underscore the crucial need to account for response biases in empathy evaluations and in other psychological assessment measures.

Drawing on theories of deliberative democracy, this article analyzes initiatives in educational settings to develop deliberative capabilities--defined here as a person's motivation and abilities to explain their views based on thoughtful considerations, reciprocal engagement, and more inclusive and respectful communication. Building and expanding on previous education experiences and citizen assemblies, we propose an integrated approach that includes: (a) peer learning, (b) playful and performative activities, and (c) authentic discussions about controversial political issues in small groups. Our field experiment involving more than 500 public school students in Brazil is used to illustrate combinations of methods to develop conceptual understandings and promote practice and self-reflection. We claim that these methods should not be conducted separately, but different combinations can complement each other to achieve better results in different contexts. This article has theoretical and practical implications for programs that seek to promote democratic communication, in particular through improving deliberative skills.

Abstract The COVID-19 pandemic and the Russian invasion of Ukraine have led to unseen disruptions in the global energy markets since the end of 2021. Residential renewable investments like photovoltaic systems, battery home storage systems, and heat pumps are therefore gaining traction. However, the benefits of those technologies during the energy crisis and beyond have not been fully quantified yet. Therefore, in this study, we benchmark renewable investments for a broad variety of single-family homes by evaluating potential cost savings and emission reductions. In addition, the study considers the influence of recent political incentives and subsidies. The results show that photovoltaic systems are a no-regret investment decision, both economically and environmentally. At the climax of the energy crisis, a typical German household with a heat pump could save 1850 € and reduce equivalent CO2 emissions by 250 g/kWh annually. Politically introduced price breaks on electricity and natural gas do not reverse this advantage. Furthermore, when owning an electric vehicle renewable investments are often more beneficial.

Abstract The current experiments searching for neutrinoless double- $$\beta $$ β ( $$0\nu \beta \beta $$ 0 ν β β ) decay also collect large statistics of Standard Model allowed two-neutrino double- $$\beta $$ β ( $$2\nu \beta \beta $$ 2 ν β β ) decay events. These can be used to search for Beyond Standard Model (BSM) physics via $$2\nu \beta \beta $$ 2 ν β β decay spectral distortions. 100Mo has a natural advantage due to its relatively short half-life, allowing higher $$2\nu \beta \beta $$ 2 ν β β decay statistics at equal exposures compared to the other isotopes. We demonstrate the potential of the dual read-out bolometric technique exploiting a 100Mo exposure of 1.47 kg $$\times $$ × years, acquired in the CUPID-Mo experiment at the Modane underground laboratory (France). We set limits on $$0\nu \beta \beta $$ 0 ν β β decays with the emission of one or more Majorons, on $$2\nu \beta \beta $$ 2 ν β β decay with Lorentz violation, and $$2\nu \beta \beta $$ 2 ν β β decay with a sterile neutrino emission. In this analysis, we investigate the systematic uncertainty induced by modeling the $$2\nu \beta \beta $$ 2 ν β β decay spectral shape parameterized through an improved model, an effect never considered before. This work motivates searches for BSM processes in the upcoming CUPID experiment, which will collect the largest amount of $$2\nu \beta \beta $$ 2 ν β β decay events among the next-generation experiments.

This article deals with the deepening of educational inequalities in rural schools after the Covid-19 pandemic, resulting in the closure of schools, downgrading of curricula and teacher training, reforms in educational policies and legislation that adjust the formation of the working class to business capitalist logic. The objective of this work is to analyze the contradictions faced by rural schools within the scope of the organization of pedagogical work, based on the attacks derived and/or deepened by the pandemic. It presents overcoming foundations based on historical-critical pedagogy and cultural-historical psychology regarding the lack of training for rural workers and confronting educational inequalities exacerbated by the pandemic. It also points out that the pandemic deepened and highlighted the roots of this process of lowering school education, especially in public schools in/from the countryside.

Abstract Digital health is increasingly promoting open health data. Although this open approach promises a number of benefits, it also leads to tensions with Indigenous data sovereignty movements led by Indigenous peoples around the world who are asserting control over the use of health data as a part of self-determination. Digital health has a role in improving access to services and delivering improved health outcomes for Indigenous communities. However, we argue that in order to be effective and ethical, it is essential that the field engages more with Indigenous peoples´ rights and interests. We discuss challenges and possible improvements for data acquisition, management, analysis, and integration as they pertain to the health of Indigenous communities around the world.

Concrete is one of the most widely used materials in the world. Still, its production processes, energy consumption, and high use of raw materials make it one of the most environmentally harmful materials. This study aims to enhance the sustainability of concrete by reducing the amount of binder and incorporating secondary materials into the cementitious matrix. The binder reduction is achieved by using a foaming agent that creates a microporous matrix, significantly decreasing the volume of cement in the material. Additionally, reinforcing the material with sheep wool fiber not only improves its mechanical properties but also gives a new purpose to a commonly discarded secondary material. The research specifically seeks to identify the most effective treatments for sheep wool fiber (including non-treated, salt-treated, lime-treated, NaOH-treated, and surfactant-treated fibers), as well as the optimal fiber length (6, 12, and 20 mm) and content (4.5, 9, and 15 kg/m³) for ultralightweight foamed concrete in terms of mechanical strength. The findings demonstrate excellent compatibility between wool fibers and ultralightweight foamed concrete, with fiber-reinforced samples showing up to a 60% increase in flexural strength and up to a 50% increase in compressive strength. Among the various fiber treatments evaluated, surfactant-treated fibers yielded the best results.

ABSTRACT Introduction Risk‐reducing salpingo‐oophorectomy (RRSO) is recommended by international guidelines in women with BRCA1/2 germline pathogenic variants (PV) to prevent ovarian cancer. Despite the solid recommendation, women frequently refuse surgery and uptake rates reported in the literature are diverse. This study analyses the uptake rate of RRSO in BRCA 1/2 PV‐carriers referred to a specialised referral centre for first counselling and investigate personal factors linked to the decision. Methods This is a single‐centre prospective study of BRCA1/2 PV‐carriers referred for the first counselling to IRCCS Fondazione San Gerardo de’ Tintori (Monza, Italy) between January 2010 and May 2023. Depending on individual characteristics, women were either proposed RRSO or surveillance, consisting of transvaginal ultrasound and CA125 measurement twice per year according to Regional guidelines. Women within the centre have access to a clinical psychologist, a nutritional consult and treatment of menopausal atrophy with diode vaginal laser. The primary endpoint of the study was the uptake rate of RRSO. The secondary objective was to evaluate the main reasons for refusing surgery. Results Among the 287 women included, surgery was proposed to 205 women either at first counselling or during surveillance and was accepted by 197, with an uptake rate of 96.1%. 17.25% of women met the psychologist before or after surgery. The main reasons for refusing RRSO were fear of iatrogenic menopause and childbearing desire. Conclusion This study shows a high uptake rate of RRSO in BRCA PV‐carriers. We believe that the presence of a dedicated outpatient clinic with a multidisciplinary team contributes decisively to our results. Gynaecologic surveillance, as though not beneficial in terms of oncological prevention, may play a significant role in encouraging women with BRCA PV to opt for risk‐reducing surgery.

Background: COVID-19 reduces lung functionality causing a decrease of blood oxygen levels (hypoxemia) often related to a decreased cellular oxygenation (hypoxia). Besides lung injury, other factors are implicated in the regulation of oxygen availability such as pH, partial arterial carbon dioxide tension (PaCO2), temperature, and erythrocytic 2,3-bisphosphoglycerate (2,3-BPG) levels, all factors affecting hemoglobin saturation curve. However, few data are currently available regarding the 2,3-BPG modulation in SARS-CoV-2 affected patients at the hospital admission. Material and methods: Sixty-eight COVID-19 patients were enrolled at hospital admission. The lung involvement was quantified using chest-Computer Tomography (CT) analysed with automatic software (CALIPER). Haemoglobin concentrations, glycemia, and routine analysis were evaluated in the whole blood, while partial arterial oxygen tension (PaO2), PaCO2, pH, and HCO3− were assessed by arterial blood gas analysis. 2,3-BPG levels were assessed by specific immunoenzymatic assays in RBCs. Results: A higher percentage of interstitial lung disease (ILD) and vascular pulmonary-related structure (VRS) volume on chest-CT quantified with CALIPER had been found in COVID-19 patients with a worse disease outcome (R = 0.4342; and R = 0.3641, respectively). Furthermore, patients with lower PaO2 showed an imbalanced acid-base equilibrium (pH, p = 0.0208; PaCO2, p = 0.0496) and a higher 2,3-BPG levels (p = 0.0221). The 2,3-BPG levels were also lower in patients with metabolic alkalosis (p = 0.0012 vs. no alkalosis; and p = 0.0383 vs. respiratory alkalosis). Conclusions: Overall, the data reveal a different pattern of activation of blood oxygenation compensatory mechanisms reflecting a different course of the COVID-19 disease specifically focusing on 2,3-BPG modulation.

A Disintegrin And Metalloproteinase domain-containing protein 10 (ADAM10), is involved in several metabolic and inflammatory pathways. We speculated that ADAM10 plays a modulatory role in adipose tissue inflammation and metabolism. To this end, we studied adipose tissue-specific ADAM10 knock-out mice (aKO). While young, regular chow diet-fed aKO mice showed increased insulin sensitivity, following prolonged (33 weeks) high-fat diet (HFD) exposure, aKO mice developed obesity and insulin resistance. Compared to controls, aKO mice showed less inflammatory adipokine profile despite the significant increase in adiposity. In brown adipose tissue, aKO mice on HFD had changes in CD8+ T cell populations indicating a lesser inflammatory pattern. Following HFD, both aKO and control littermates demonstrated decreased adipose tissue pro-inflammatory macrophages, and increased anti-inflammatory accumulation, without differences between the genotypes. Collectively, our observations indicate that selective deletion of ADAM10 in adipocytes results in a mitigated inflammatory response, leading to increased insulin sensitivity in young mice fed with regular diet. This state of insulin sensitivity, following prolonged HFD, facilitates energy storage resulting in increased fat accumulation which ultimately leads to the development of a phenotype of obesity and insulin resistance. In conclusion, the data indicate that ADAM10 has a modulatory effect of inflammation and whole-body energy metabolism.

OBJECTIVES:. Septic shock is a common condition necessitating timely management including hemodynamic support with vasopressors. Despite the high prevalence and mortality, there is limited data characterizing patients who require three or more vasopressors. We sought to define the demographics, outcomes, and prognostic determinants associated with septic shock requiring three or more vasopressors. DESIGN:. This is a multicenter retrospective cohort of two ICU databases, Medical Information Mart for Intensive Care IV (MIMIC-IV) and electronic ICU-Clinical Research Database, which include over 400,000 patients admitted to 342 ICUs. PATIENTS:. Inclusion criteria entailed patients who were: 1) age 18 years old and older, 2) admitted to any ICU, 3) administered at least three vasopressors for at least 2 hours at any time during their ICU stay, and 4) identified to have sepsis based on the Sepsis-3 criteria. INTERVENTIONS:. None. MEASUREMENTS AND MAIN RESULTS:. A total of 3447 patients met inclusion criteria. The median age was 67 years, 60.5% were male, and 96.6% had full code orders at the time of the third vasopressor initiation. Septic shock requiring three or more vasopressors was associated with 57.6% in-hospital mortality. Code status changes occurred in 23.9% of patients following initiation of a third vasopressor. Elevated lactate upon ICU admission (odds ratio [95% CI], 2.79 [2.73–2.85]), increased duration of time between ICU admission and third vasopressor initiation (1.78 [1.69–1.87]), increased serum creatinine (1.61 [1.59–1.62]), and age above 60 years (1.47 [1.41–1.54]) were independently associated with an increased risk of mortality based on analysis of the MIMIC-IV database. Non-White race and Richmond Agitation-Sedation Scale scores were not associated with mortality. CONCLUSIONS:. Septic shock requiring three vasopressors is associated with exceptionally high mortality. Knowledge of patients at highest risk of mortality in this population may inform management and expectations conveyed in shared decision-making.

Large language models (LLMs) continue to exhibit noteworthy capabilities across a spectrum of areas, including emerging proficiencies across the health care continuum. Successful LLM implementation and adoption depend on digital readiness, modern infrastructure, a trained workforce, privacy, and an ethical regulatory landscape. These factors can vary significantly across health care ecosystems, dictating the choice of a particular LLM implementation pathway. This perspective discusses 3 LLM implementation pathways—training from scratch pathway (TSP), fine-tuned pathway (FTP), and out-of-the-box pathway (OBP)—as potential onboarding points for health systems while facilitating equitable adoption. The choice of a particular pathway is governed by needs as well as affordability. Therefore, the risks, benefits, and economics of these pathways across 4 major cloud service providers (Amazon, Microsoft, Google, and Oracle) are presented. While cost comparisons, such as on-demand and spot pricing across the cloud service providers for the 3 pathways, are presented for completeness, the usefulness of managed services and cloud enterprise tools is elucidated. Managed services can complement the traditional workforce and expertise, while enterprise tools, such as federated learning, can overcome sample size challenges when implementing LLMs using health care data. Of the 3 pathways, TSP is expected to be the most resource-intensive regarding infrastructure and workforce while providing maximum customization, enhanced transparency, and performance. Because TSP trains the LLM using enterprise health care data, it is expected to harness the digital signatures of the population served by the health care system with the potential to impact outcomes. The use of pretrained models in FTP is a limitation. It may impact its performance because the training data used in the pretrained model may have hidden bias and may not necessarily be health care–related. However, FTP provides a balance between customization, cost, and performance. While OBP can be rapidly deployed, it provides minimal customization and transparency without guaranteeing long-term availability. OBP may also present challenges in interfacing seamlessly with downstream applications in health care settings with variations in pricing and use over time. Lack of customization in OBP can significantly limit its ability to impact outcomes. Finally, potential applications of LLMs in health care, including conversational artificial intelligence, chatbots, summarization, and machine translation, are highlighted. While the 3 implementation pathways discussed in this perspective have the potential to facilitate equitable adoption and democratization of LLMs, transitions between them may be necessary as the needs of health systems evolve. Understanding the economics and trade-offs of these onboarding pathways can guide their strategic adoption and demonstrate value while impacting health care outcomes favorably.

COVID-19 y preeclampsia

BackgroundCardiovascular diseases remain a leading cause of morbidity and mortality worldwide and require extensive investigation through in-vitro studies. Mock Circulatory Loops (MCLs) are advanced in-vitro platforms that accurately replicate physiological and pathological hemodynamic conditions, while also allowing for precise and patient-specific data collection. Particle Image Velocimetry (PIV) is the standard flow visualization technique for in-vitro studies, but it is costly and requires strict safety measures. High-power Light Emitting Diode illuminated PIV (LED-PIV) offers a safer and cheaper alternative.MethodsIn this study, we aim to demonstrate the feasibility of a Hybrid-MCL integrated with a LED-PIV system for the investigation of Abdominal Aortic Aneurysm (AAA) compliant phantoms. We considered two distinct AAA models, namely, an idealized model and a patient-specific one under different physiological flow and pressure conditions.ResultsThe efficacy of the proposed setup for the investigation of AAA hemodynamics was confirmed by observing velocity and vorticity fields across multiple flow rate scenarios and regions of interest.ConclusionThe findings of this study underscore the potential impact of Hybrid-MCL integrated with a LED-PIV system on enhancing the affordability, accessibility, and safety of in-vitro CVD investigations.

La creación de una sociedad inclusiva implica reconocer y promover los derechos fundamentales de todas las personas, independientemente de sus capacidades. En este contexto, resulta imperativo abordar de manera específica el derecho al trabajo de las personas con discapacidad. En el presente artículo se analiza el derecho al trabajo de las personas con discapacidad en el Ecuador, como un derecho fundamental inherente a la dignidad del ser humano, ya que su reconocimiento involucra un sinnúmero de toma de decisiones y políticas públicas por parte del Estado ecuatoriano encaminadas a eliminar barreras y adaptar el entorno social que permita lograr una efectiva inserción al trabajo de este grupo de personas de atención prioritaria. El objetivo central es identificar las barreras que enfrentan en el ámbito laboral las personas con discapacidad así como las mejores prácticas y políticas que han demostrado ser efectivas en diversos contextos, con el fin de promover un entorno laboral inclusivo y equitativo. Para ello, se ha aplicado un análisis comparativo, que permita contrastar las políticas y prácticas de inclusión laboral ejecutadas dentro de nuestro país, identificando los factores clave que contribuyen al éxito de dichas iniciativas.

Healthcare AI faces an ethical dilemma between selective and equitable deployment, exacerbated by flawed performance metrics. These metrics inadequately capture real-world complexities and biases, leading to premature assertions of effectiveness. Improved evaluation practices, including continuous monitoring and silent evaluation periods, are crucial. To address these fundamental shortcomings, a paradigm shift in AI assessment is needed, prioritizing actual patient outcomes over conventional benchmarking.

El objetivo del estudio fue analizar la importancia de la neurociencia en Iberoamérica para el proceso de la enseñanza-aprendizaje en la etapa preescolar. Se realizó una revisión sistemática narrativa en las bases de datos SciELO, Redalyc, Dialnet, REDIB y la revista Latinoamericana de Educación Infantil (RELAdEI), utilizando como criterio de búsqueda las palabras neurociencia, proceso de aprendizaje, neuroeducación desde 2018 hasta 2023. La revisión se centró principalmente en artículos publicados entre 2019 y 2023 (94 %), reflejando un enfoque muy actualizado sobre la neuroeducación infantil. El año con más aportes fue 2019 (26%), seguido de cerca por 2021 (24%). Este conjunto de datos permite analizar el estado del arte reciente sobre la integración de neurociencia en el proceso de enseñanza-aprendizaje temprano en América Latina. Se evidencia el creciente interés investigativo en esta síntesis emergente de disciplinas para potenciar la pedagogía. Se concluyó la integración progresiva de la neurociencia a las prácticas pedagógicas de la primera infancia. El análisis de la producción científica reciente a partir de 2019 constata el vertiginoso incremento de investigaciones sobre la aplicación de conocimientos neurobiológicos para transformar y potenciar los procesos de enseñanza-aprendizaje temprano.

Objective: To assess the quality and readability of online information on holmium laser enucleation of the prostate in managing benign prostate hyperplasia using the most-used search engine worldwide, Google. Methods: Google search terms “Holmium laser surgery” and “enlarged prostate” were used to generate 150 search results. Two independent authors (i) excluded any paywall, scientific literature, or advertisement and (ii) conducted an independent assessment on information quality, which was based on DISCERN, QUEST, and JAMA criteria, and readability, which was based on the FKG, GFI, SMOG, and FRE scores on qualified webpages. A third author was involved if there were any discrepancies between the assessments. Results: 107 qualified webpages were included in the data analysis. The median DISCERN score was 42 out of 80 (IQR 35–49). The median JAMA score was 0 out of 4 (IQR 0–1). The median QUEST score was 9 out of 28 (IQR 9–12). Using the non-parametric ANOVA and post hoc Games–Howell test, significant differences were identified between rankings of webpages. Sponsorship had no influence on the quality of webpages. The overall readability level required a minimum reading level of grade 11. Linear regression analysis showed that a higher ranked webpage is a positive predictor for all three quality assessment tools. Conclusions: The overall quality of online information on HoLEP is poor. We identify that the top-ranked google searches have a higher DISCERN score and are a positive predictor for DISCERN/QUEST/JAMA. Quality online information can benefit patients but should be used in conjunction with professional medical consultation.

Abstract We present the INSPIRE dataset, a publicly available research dataset in perioperative medicine, which includes approximately 130,000 surgical operations at an academic institution in South Korea over a ten-year period between 2011 and 2020. This comprehensive dataset includes patient characteristics such as age, sex, American Society of Anesthesiologists physical status classification, diagnosis, surgical procedure code, department, and type of anaesthesia. The dataset also includes vital signs in the operating theatre, general wards, and intensive care units (ICUs), laboratory results from six months before admission to six months after discharge, and medication during hospitalisation. Complications include total hospital and ICU length of stay and in-hospital death. We hope this dataset will inspire collaborative research and development in perioperative medicine and serve as a reproducible external validation dataset to improve surgical outcomes.

Abstract In low- and middle-income countries, the substantial costs associated with traditional data collection pose an obstacle to facilitating decision-making in the field of public health. Satellite imagery offers a potential solution, but the image extraction and analysis can be costly and requires specialized expertise. We introduce SatelliteBench, a scalable framework for satellite image extraction and vector embeddings generation. We also propose a novel multimodal fusion pipeline that utilizes a series of satellite imagery and metadata. The framework was evaluated generating a dataset with a collection of 12,636 images and embeddings accompanied by comprehensive metadata, from 81 municipalities in Colombia between 2016 and 2018. The dataset was then evaluated in 3 tasks: including dengue case prediction, poverty assessment, and access to education. The performance showcases the versatility and practicality of SatelliteBench, offering a reproducible, accessible and open tool to enhance decision-making in public health.

Radiology has a number of characteristics that make it an especially suitable medical discipline for early artificial intelligence (AI) adoption. These include having a well-established digital workflow, standardized protocols for image storage, and numerous well-defined interpretive activities. The more than 200 commercial radiologic AI-based products recently approved by the Food and Drug Administration (FDA) to assist radiologists in a number of narrow image-analysis tasks such as image enhancement, workflow triage, and quantification, corroborate this observation. However, in order to leverage AI to boost efficacy and efficiency, and to overcome substantial obstacles to widespread successful clinical use of these products, radiologists should become familiarized with the emerging applications in their particular areas of expertise. In light of this, in this article we survey the existing literature on the application of AI-based techniques in neuroradiology, focusing on conditions such as vascular diseases, epilepsy, and demyelinating and neurodegenerative conditions. We also introduce some of the algorithms behind the applications, briefly discuss a few of the challenges of generalization in the use of AI models in neuroradiology, and skate over the most relevant commercially available solutions adopted in clinical practice. If well designed, AI algorithms have the potential to radically improve radiology, strengthening image analysis, enhancing the value of quantitative imaging techniques, and mitigating diagnostic errors.

Abstract Pulse oximeters measure peripheral arterial oxygen saturation (SpO2) noninvasively, while the gold standard (SaO2) involves arterial blood gas measurement. There are known racial and ethnic disparities in their performance. BOLD is a dataset that aims to underscore the importance of addressing biases in pulse oximetry accuracy, which disproportionately affect darker-skinned patients. The dataset was created by harmonizing three Electronic Health Record databases (MIMIC-III, MIMIC-IV, eICU-CRD) comprising Intensive Care Unit stays of US patients. Paired SpO2 and SaO2 measurements were time-aligned and combined with various other sociodemographic and parameters to provide a detailed representation of each patient. BOLD includes 49,099 paired measurements, within a 5-minute window and with oxygen saturation levels between 70–100%. Minority racial and ethnic groups account for ~25% of the data – a proportion seldom achieved in previous studies. The codebase is publicly available. Given the prevalent use of pulse oximeters in the hospital and at home, we hope that BOLD will be leveraged to develop debiasing algorithms that can result in more equitable healthcare solutions.

Abstract The concept of synthetic dimensions works particularly well in atomic physics, quantum optics, and photonics, where the internal degrees of freedom (Zeeman sublevels of the ground state, metastable excited states, or motional states for atoms, and angular momentum states or transverse modes for photons) provide the synthetic space. In this Perspective article we report on recent progress on studies of synthetic dimensions, mostly, but not only, based on the research realized around the Barcelona groups (ICFO, UAB), Donostia (DIPC), Poznan (UAM), Kraków (UJ), and Allahabad (HRI). We describe our attempts to design quantum simulators with synthetic dimensions, to mimic curved spaces, artificial gauge fields, lattice gauge theories, twistronics, quantum random walks, and more.

ABSTRACT: Heifers (n = 40) were randomly allocated to 5 treatment groups: (1) control (no additives); (2) virginiamycin (VM; 200 mg/d); (3) monensin (MT; 200 mg/d) + tylosin (110 mg/d); (4) monensin (MLY; 220 mg/d) + live yeast (5.0 × 108 cfu/d); (5) sodium bicarbonate (BUF; 200 g/d) + magnesium oxide (30 g/d).

Abstract Acute pancreatitis (AP) is an acute inflammatory disorder that is common, costly, and is increasing in incidence worldwide with over 300,000 hospitalizations occurring yearly in the United States alone. As its course and outcomes vary widely, a critical knowledge gap in the field has been a lack of accurate prognostic tools to forecast AP patients’ outcomes. Despite several published studies in the last three decades, the predictive performance of published prognostic models has been found to be suboptimal. Recently, non-regression machine learning models (ML) have garnered intense interest in medicine for their potential for better predictive performance. Each year, an increasing number of AP models are being published. However, their methodologic quality relating to transparent reporting and risk of bias in study design has never been systematically appraised. Therefore, through collaboration between a group of clinicians and data scientists with appropriate content expertise, we will perform a systematic review of papers published between January 2021 and December 2023 containing artificial intelligence prognostic models in AP. To systematically assess these studies, the authors will leverage the CHARMS checklist, PROBAST tool for risk of bias assessment, and the most current version of the TRIPOD-AI. (Research Registry ( http://www.reviewregistry1727 .).