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Background: An accurate prognostic tool is essential to aid clinical decision-making (e.g., patient triage) and to advance personalized medicine. However, such a prognostic tool is lacking for acute pancreatitis (AP). Increasingly machine learning (ML) techniques are being used to develop high-performing prognostic models in AP. However, methodologic and reporting quality has received little attention. High-quality reporting and study methodology are critical for model validity, reproducibility, and clinical implementation. In collaboration with content experts in ML methodology, we performed a systematic review critically appraising the quality of methodology and reporting of recently published ML AP prognostic models., Methods/findings: Using a validated search strategy, we identified ML AP studies from the databases MEDLINE and EMBASE published between January 2021 and December 2023. We also searched pre-print servers medRxiv, bioRxiv, and arXiv for pre-prints registered between January 2021 and December 2023. Eligibility criteria included all retrospective or prospective studies that developed or validated new or existing ML models in patients with AP that predicted an outcome following an episode of AP. Meta-analysis was considered if there was homogeneity in the study design and in the type of outcome predicted. For risk of bias (ROB) assessment, we used the Prediction Model Risk of Bias Assessment Tool. Quality of reporting was assessed using the Transparent Reporting of a Multivariable Prediction Model of Individual Prognosis or Diagnosis-Artificial Intelligence (TRIPOD+AI) statement that defines standards for 27 items that should be reported in publications using ML prognostic models. The search strategy identified 6,480 publications of which 30 met the eligibility criteria. Studies originated from China (22), the United States (4), and other (4). All 30 studies developed a new ML model and none sought to validate an existing ML model, producing a total of 39 new ML models. AP severity (23/39) or mortality (6/39) were the most common outcomes predicted. The mean area under the curve for all models and endpoints was 0.91 (SD 0.08). The ROB was high for at least one domain in all 39 models, particularly for the analysis domain (37/39 models). Steps were not taken to minimize over-optimistic model performance in 27/39 models. Due to heterogeneity in the study design and in how the outcomes were defined and determined, meta-analysis was not performed. Studies reported on only 15/27 items from TRIPOD+AI standards, with only 7/30 justifying sample size and 13/30 assessing data quality. Other reporting deficiencies included omissions regarding human-AI interaction (28/30), handling low-quality or incomplete data in practice (27/30), sharing analytical codes (25/30), study protocols (25/30), and reporting source data (19/30)., Conclusions: There are significant deficiencies in the methodology and reporting of recently published ML based prognostic models in AP patients. These undermine the validity, reproducibility, and implementation of these prognostic models despite their promise of superior predictive accuracy., Registration: Research Registry (reviewregistry1727)., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2025 Critelli et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Over the past 2 decades, exponential growth in data availability, computational power, and newly available modeling techniques has led to an expansion in interest, investment, and research in Artificial Intelligence (AI) applications. Ophthalmology is one of many fields that seek to benefit from AI given the advent of telemedicine screening programs and the use of ancillary imaging. However, before AI can be widely deployed, further work must be done to avoid the pitfalls within the AI lifecycle. This review article breaks down the AI lifecycle into seven steps-data collection; defining the model task; data preprocessing and labeling; model development; model evaluation and validation; deployment; and finally, post-deployment evaluation, monitoring, and system recalibration-and delves into the risks for harm at each step and strategies for mitigating them., Competing Interests: LAC is the Editor-In-Chief of PLOS Digital Health., (Copyright: © 2024 Nakayama et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

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 .)., (© 2024. The Author(s).)

Selection bias can arise through many aspects of a study, including recruitment, inclusion/exclusion criteria, input-level exclusion and outcome-level exclusion, and often reflects the underrepresentation of populations historically disadvantaged in medical research. The effects of selection bias can be further amplified when non-representative samples are used in artificial intelligence (AI) and machine learning (ML) applications to construct clinical algorithms. Building on the "Data Cards" initiative for transparency in AI research, we advocate for the addition of a participant flow diagram for AI studies detailing relevant sociodemographic and/or clinical characteristics of excluded participants across study phases, with the goal of identifying potential algorithmic biases before their clinical implementation. We include both a model for this flow diagram as well as a brief case study explaining how it could be implemented in practice. Through standardized reporting of participant flow diagrams, we aim to better identify potential inequities embedded in AI applications, facilitating more reliable and equitable clinical algorithms., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Artificial intelligence (AI) algorithms have the potential to revolutionize healthcare, but their successful translation into clinical practice has been limited. One crucial factor is the data used to train these algorithms, which must be representative of the population. However, most healthcare databases are derived from high-income countries, leading to non-representative models and potentially exacerbating health inequities. This review focuses on the landscape of health-related open datasets in Latin America, aiming to identify existing datasets, examine data-sharing frameworks, techniques, platforms, and formats, and identify best practices in Latin America. The review found 61 datasets from 23 countries, with the DATASUS dataset from Brazil contributing to the majority of articles. The analysis revealed a dearth of datasets created by the authors themselves, indicating a reliance on existing open datasets. The findings underscore the importance of promoting open data in Latin America. We provide recommendations for enhancing data sharing in the region., Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: Leo Anthony Celi is the Editor-in Chief of PLOS Digital Health., (Copyright: © 2023 Restrepo et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Current methods to evaluate a journal's impact rely on the downstream citation mapping used to generate the Impact Factor. This approach is a fragile metric prone to being skewed by outlier values and does not speak to a researcher's contribution to furthering health outcomes for all populations. Therefore, we propose the implementation of a Diversity Factor to fulfill this need and supplement the current metrics. It is composed of four key elements: dataset properties, author country, author gender and departmental affiliation. Due to the significance of each individual element, they should be assessed independently of each other as opposed to being combined into a simplified score to be optimized. Herein, we discuss the necessity of such metrics, provide a framework to build upon, evaluate the current landscape through the lens of each key element and publish the findings on a freely available website that enables further evaluation. The OpenAlex database was used to extract the metadata of all papers published from 2000 until August 2022, and Natural language processing was used to identify individual elements. Features were then displayed individually on a static dashboard developed using TableauPublic, which is available at www.equitablescience.com. In total, 130,721 papers were identified from 7,462 journals where significant underrepresentation of LMIC and Female authors was demonstrated. These findings are pervasive and show no positive correlation with the Journal's Impact Factor. The systematic collection of the Diversity Factor concept would allow for more detailed analysis, highlight gaps in knowledge, and reflect confidence in the translation of related research. Conversion of this metric to an active pipeline would account for the fact that how we define those most at risk will change over time and quantify responses to particular initiatives. Therefore, continuous measurement of outcomes across groups and those investigating those outcomes will never lose importance. Moving forward, we encourage further revision and improvement by diverse author groups in order to better refine this concept., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Gallifant et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

To examine the effects of gemfibrozil on very-low-density lipoprotein (VLDL) composition and low-density lipoprotein (LDL) size, five men with hypertriglyceridemia (HTG) alone and five men with HTG and hypercholesterolemia (combined hyperlipidemia, CHLP) were randomized for 8 weeks to Lopid SR (slow-release gemfibrozil; two 600-mg tablets once per day) or placebo in a crossover study. Drug therapy versus placebo significantly decreased plasma triglyceride (68%), and VLDL (77%), and significantly increased high-density lipoprotein cholesterol (25%); total cholesterol, apolipoprotein B and lipoprotein[a] concentrations did not change significantly. With drug, mean total apoE in plasma was 53% lower in patients with HTG and 39% lower in patients with CHLP. Gemfibrozil significantly affected VLDL composition: protein increased 26%, molar ratio of apoE to apoB reduced 48%, apoC-II increased 19%, and apoC-III decreased 9%. LDL cholesteryl ester significantly increased with drug treatment. VLDL subfractions were separated and classified as heparin binding (VLDLR, apoE rich) or nonbinding (VLDLNR-1 and VLDLNR-2, both apoE poor). All VLDL subfractions were significantly lower with drug therapy, and the differences for total VLDL and for VLDL subfractions were greater in patients with HTG. With placebo, VLDLR accounted for 41.8% of VLDL in HTG and 49.0% of VLDL in CHLP, reduced to 27.6% and 38.6%, respectively, with gemfibrozil. Taken together, these results suggest that treatment with gemfibrozil reduces plasma concentrations of VLDL and alters the apoprotein composition of VLDL in a manner that may favor LDL- and VLDL-receptor-mediated clearance of the apoE-rich VLDL subfraction, thereby reducing TG-rich particle concentrations, and possibly reducing risk for coronary heart disease.

Growing clinical evidence suggests that metabolic behavior and atherogenic potential vary within lipoprotein subclasses that can be defined by apolipoprotein variation. Variant constituency of apolipoproteins B and E (apoB and apoE) may be particularly important because of the central roles of these apolipoproteins in the endogenous lipid delivery cascade. ApoB is the sole protein of low-density lipoprotein (LDL), and like LDL cholesterol, the plasma apoB level has been positively correlated with risk for atherosclerotic disease. ApoE is a major functional lipoprotein in the triglyceride-rich lipoproteins, and may be crucial in the conversion of very low density lipoprotein (VLDL) to LDL. Based on work by others that enabled the quantititation of apoB-containing particles by content of up to two other types of apolipoprotein, we have developed a method for determining the amount of apoE in apoB-containing lipoproteins (Lp B:E) and the amount of apoB in apoE-containing lipoproteins (Lp E:B). From the Lp B:E and Lp E:B concentrations, the molar ratio of apoE to apoB in lipoproteins containing apoB and/or apoE in plasma can be determined. The methodology is fast, specific, and sensitive and should prove extremely useful in further categorizing lipoproteins and characterizing their behavior. In applying this method to clinical groupings of normo- and hyperlipidemia, we found that the plasma triglyceride level correlated with the apoE and Lp B:E concentrations in plasma, while the total cholesterol level correlated with the apoB and Lp E:B levels.

The hypolipidaemic agent pravastatin differs from other US Food and Drug Administration (FDA)-approved HMG-CoA reductase inhibitors (e.g. lovastatin and simvastatin) because it has greater hydrophilicity, as a result of the hydroxyl group attached to its decalin ring. The hydrophilic nature of pravastatin accounts for its minimal penetration into the intracellular space of nonhepatic tissues, including an apparent inability to cross the blood-brain barrier. The drug is also well tolerated because it is rapidly absorbed and excreted, and does not accumulate in plasma even with repeated administration. Pravastatin is taken up into the liver by an active transport carrier system, and the hepatic extraction ratio is high (0.66). The drug and its metabolites are cleared through both hepatic and renal routes (53 and 47%, respectively). The dual route of elimination reduces the need for dosage adjustment if the function of either of these organs is impaired. Dosage adjustments are also not required on the basis of age or gender. Furthermore, the drug can be given without regard to food intake, an important consideration for compliance since lipid-lowering therapy is generally required long term. The drug is approximately 50% protein bound, and, therefore, compared with other members of its class the tendency for displacement of highly protein bound drugs such as warfarin is decreased. This minimal potential for drug-drug interactions is important for patients who are taking multiple drugs because of concomitant medical problems. However, as with any HMG-CoA reductase inhibitor, caution should be exercised when pravastatin is given with nicotinic acid (niacin), gemfibrozil or cyclosporin, because of increased risk for myopathy in patients receiving combination therapy.