Journal of Clinical Epidemiology最新文献

{"title":"\"We used standard Cochrane methods\" - observational study on reporting according to PRISMA-A in Cochrane review abstracts between 2016 and 2023.","authors":"Kathrin Wandscher, Jasmin Helbach, Dawid Pieper, Falk Hoffmann","doi":"10.1016/j.jclinepi.2025.111713","DOIUrl":"https://doi.org/10.1016/j.jclinepi.2025.111713","url":null,"abstract":"<p><strong>Objectives: </strong>To evaluate reporting of abstracts of Cochrane reviews (CRs) according to PRISMA-A over the last years and trends in reporting the use of standard Cochrane methods and GRADE.</p><p><strong>Study design and setting: </strong>This was a retrospective, observational study based on eligible CRs indexed in MEDLINE (via PubMed) from 2016 to 2023. Stratified random sample with a total of 520 abstracts was drawn and 489 CRs on effectiveness were included. PRISMA-A adherence, reporting use of standard methods and GRADE were extracted by two reviewers independently. Data were analyzed descriptively and stratified by publication year and reporting of standard methods.</p><p><strong>Results: </strong>Mean score of fully reported PRISMA-A items ranged from 6.9 to 7.2 out of 12 between 2016 and 2023, while abstract length increased from a median of 686 to 866 words, particularly in the results section. Over the years, reporting that standard methods were used increased from 27.4% to 53.2% and for GRADE from 30.7% to 74.2%. Abstracts reporting the use of standard methods more often adhere to individual PRISMA-A items on results and less often on methods with no differences in overall PRISMA-A adherence.</p><p><strong>Conclusion: </strong>PRISMA-A adherence in CR abstracts has remained unchanged over the years, with reporting more information on results than methods. This conflicts with PRISMA-A, which recommends specifying the methods used, and forms the basis for reporting abstracts in the Cochrane Handbook since August 2023. Therefore, Cochrane authors and editors should closely monitor the reporting whether standard methods were used.</p>","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":" ","pages":"111713"},"PeriodicalIF":7.3,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143384027","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Potential interactions between digoxin and direct oral anticoagulants: application of cohort & novel case-crossover designs","authors":"Angel Y.S. Wong ,&nbsp;Charlotte Warren-Gash ,&nbsp;Krishnan Bhaskaran ,&nbsp;Clémence Leyrat ,&nbsp;Amitava Banerjee ,&nbsp;Liam Smeeth ,&nbsp;Ian J. Douglas","doi":"10.1016/j.jclinepi.2025.111709","DOIUrl":"10.1016/j.jclinepi.2025.111709","url":null,"abstract":"<div><h3>Objectives</h3><div>Direct oral anticoagulants (DOACs) are commonly co-prescribed with digoxin, but whether there is a drug interaction between them is unclear. We aimed to investigate potential drug interactions between DOACs and digoxin.</div></div><div><h3>Study Design and Setting</h3><div>We identified DOAC users during January 1, 2011–December 31, 2019 using data from Clinical Practice Research Datalink Aurum in cohort design with propensity score to compare the hazards of effectiveness cardiovascular and mortality outcomes and safety bleeding outcomes, respectively, in DOAC + digoxin users versus DOAC + beta-blocker users. A case-crossover design was conducted to compare odds of exposure to different drug initiation patterns in hazard period versus referent period.</div></div><div><h3>Results</h3><div>Of 397,459 DOAC users, we identified 25,251 co-prescribed digoxin and 109,779 co-prescribed beta-blockers in cohort study. A lower proportion of DOAC + digoxin users were men (46%) in contrast with that of DOAC + beta-blocker users (53%). Mean age of DOAC + digoxin users (77.1 years) were higher than DOAC + beta-blocker users (74.5 years). No increased risk of pharmacologically predictable DOAC safety outcomes or specific effectiveness outcomes was seen with DOAC + digoxin. A higher risk of all-cause mortality (hazard ratio: 1.35; 99% confidence interval [CI]: 1.14–1.61) was observed with DOAC + digoxin versus DOAC + beta-blockers. In the case-crossover study, a 24% higher odds of all-cause mortality was seen with initiating digoxin while taking DOAC (odds ratio: 1.24; 99% CI: 1.06–1.45); and a 63% higher odds was also seen with initiating DOAC while taking digoxin (odds ratio: 1.63; 99% CI: 1.41–1.88).</div></div><div><h3>Conclusion</h3><div>We found no increased risk of bleeding when DOACs are used with digoxin, suggesting combined use does not lead to drug-drug interaction. Future work is recommended to investigate the underlying mechanism of association with all-cause mortality.</div></div><div><h3>Plain Language Summary</h3><div>This study aimed to examine potential drug interactions between direct oral anticoagulants (DOACs) (a drug class to prevent blood clots) and digoxin (treatment of abnormal heart rhythms). We compared a range of clinical outcomes in people prescribed DOAC and digoxin with people prescribed DOAC and beta-blockers (a treatment alternative to digoxin). We also used a new study design (case-crossover design) to compare the risk of clinical outcomes between different periods within a person as a validation. In both study designs, we found no increased risk of bleeding when DOACs are used with digoxin, suggesting combined use does not lead to drug-drug interaction. However, we found an increased risk of all-cause death associated with digoxin in DOAC users which requires further investigation.</div></div>","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":"181 ","pages":"Article 111709"},"PeriodicalIF":7.3,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143374911","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Income inequality and access to advanced immunotherapy for lung cancer: the case of Durvalumab in the Netherlands","authors":"Dimitris Katsimpokis ,&nbsp;Mieke J. Aarts ,&nbsp;Gijs Geleijnse ,&nbsp;Peter Kunst ,&nbsp;Maarten J. Bijlsma","doi":"10.1016/j.jclinepi.2025.111711","DOIUrl":"10.1016/j.jclinepi.2025.111711","url":null,"abstract":"<div><h3>Background and Objectives</h3><div>With the introduction of immunotherapy with nonsmall cell lung cancer, prognosis of these patients has improved. However, socioeconomic differences in access to various immunotherapy treatments have been reported. In the Netherlands, such differences are not expected due to universal insurance coverage.</div></div><div><h3>Study Design and Setting</h3><div>We investigated the existence of differential susceptibility by socioeconomic status (SES) of the effect of distance to treatment hospital on access to Durvalumab in patients with stage III nonsmall cell lung cancer who received chemoradiation, and the influence of differential mortality. We used data from the Netherlands Cancer Registry (<em>n</em> = 3774) from the period 2017–2021. First, we fitted Bayesian discrete failure time models and compared SES-by-distance-to-hospital interaction to a baseline model including age, distance, SES, and performance score. We then fitted a time to mortality model and used both models in a g-formula to simulate a scenario where mortality levels were equalized.</div></div><div><h3>Results</h3><div>Our results showed that the high SES group received Durvalumab more often than the low SES group (hazard ratio = 1.26; 95% credible interval = [1.06, 1.53]), and even 4 km distance increase leads to less Durvalumab (hazard ratio = 0.93; 95% credible interval = [0.86, 0.99]). Bayes factor &lt; 3 indicated inconclusive evidence for a SES by distance interaction effect, while g-formula results showed that differential mortality does not affect SES differences. Secondary analyses showed strong evidence that SES differences in using Durvalumab were constant over the years (Bayes factor &gt; 17).</div></div><div><h3>Conclusion</h3><div>Overall, these results are significant for understanding how socioeconomic inequality affects proper care and can be vital for public policy.</div></div>","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":"181 ","pages":"Article 111711"},"PeriodicalIF":7.3,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143366525","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Updating methods for artificial intelligence–based clinical prediction models: a scoping review","authors":"Lotta M. Meijerink ,&nbsp;Zoë S. Dunias ,&nbsp;Artuur M. Leeuwenberg ,&nbsp;Anne A.H. de Hond ,&nbsp;David A. Jenkins ,&nbsp;Glen P. Martin ,&nbsp;Matthew Sperrin ,&nbsp;Niels Peek ,&nbsp;René Spijker ,&nbsp;Lotty Hooft ,&nbsp;Karel G.M. Moons ,&nbsp;Maarten van Smeden ,&nbsp;Ewoud Schuit","doi":"10.1016/j.jclinepi.2024.111636","DOIUrl":"10.1016/j.jclinepi.2024.111636","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;Objectives&lt;/h3&gt;&lt;div&gt;To give an overview of methods for updating artificial intelligence (AI)-based clinical prediction models based on new data.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Study Design and Setting&lt;/h3&gt;&lt;div&gt;We comprehensively searched Scopus and Embase up to August 2022 for articles that addressed developments, descriptions, or evaluations of prediction model updating methods. We specifically focused on articles in the medical domain involving AI-based prediction models that were updated based on new data, excluding regression-based updating methods as these have been extensively discussed elsewhere. We categorized and described the identified methods used to update the AI-based prediction model as well as the use cases in which they were used.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Results&lt;/h3&gt;&lt;div&gt;We included 78 articles. The majority of the included articles discussed updating for neural network methods (93.6%) with medical images as input data (65.4%). In many articles (51.3%) existing, pretrained models for broad tasks were updated to perform specialized clinical tasks. Other common reasons for model updating were to address changes in the data over time and cross-center differences; however, more unique use cases were also identified, such as updating a model from a broad population to a specific individual. We categorized the identified model updating methods into four categories: neural network-specific methods (described in 92.3% of the articles), ensemble-specific methods (2.5%), model-agnostic methods (9.0%), and other (1.3%). Variations of neural network-specific methods are further categorized based on the following: (1) the part of the original neural network that is kept, (2) whether and how the original neural network is extended with new parameters, and (3) to what extent the original neural network parameters are adjusted to the new data. The most frequently occurring method (&lt;em&gt;n&lt;/em&gt; = 30) involved selecting the first layer(s) of an existing neural network, appending new, randomly initialized layers, and then optimizing the entire neural network.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Conclusion&lt;/h3&gt;&lt;div&gt;We identified many ways to adjust or update AI-based prediction models based on new data, within a large variety of use cases. Updating methods for AI-based prediction models other than neural networks (eg, random forest) appear to be underexplored in clinical prediction research.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Plain Language Summary&lt;/h3&gt;&lt;div&gt;AI-based prediction models are increasingly used in health care, helping clinicians with diagnosing diseases, guiding treatment decisions, and informing patients. However, these prediction models do not always work well when applied to hospitals, patient populations, or times different from those used to develop the models. Developing new models for every situation is neither practical nor desired, as it wastes resources, time, and existing knowledge. A more efficient approach is to adjust existing models to new contexts (‘updating’),","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":"178 ","pages":"Article 111636"},"PeriodicalIF":7.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142814886","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cross-national statistical harmonization of the Center for Epidemiologic Studies Depression (CES-D) scale among older adults in China, England, India, Mexico, South Africa, and the United States","authors":"Xuexin Yu ,&nbsp;Richard N. Jones ,&nbsp;Lindsay C. Kobayashi ,&nbsp;Alden L. Gross","doi":"10.1016/j.jclinepi.2024.111623","DOIUrl":"10.1016/j.jclinepi.2024.111623","url":null,"abstract":"<div><h3>Objectives</h3><div>We examined differential item functioning (DIF) of the Center for Epidemiologic Studies Depression Scale (CES-D) items by country and statistically harmonized common cross-national factor scores for the CES-D to aid further cross-national research.</div></div><div><h3>Study Design and Setting</h3><div>Data were from Harmonized Cognitive Assessment Protocol (HCAP) studies in China (<em>N</em> = 9639), England (<em>N</em> = 1262), India (<em>N</em> = 4048), Mexico (<em>N</em> = 1918), South Africa (<em>N</em> = 631), and the United States (<em>N</em> = 3321). Multiple indicators, multiple causes models were estimated to test DIF in the CES-D items by country. DIF items were defined as having an odds ratio (OR) outside the range of 0.75–1.25 in multiple indicators, multiple causes models. We evaluated DIF impact and identified salient DIF by examining whether the difference between DIF-adjusted factor scores and non-DIF–adjusted factor scores exceeded a threshold of 0.30 standard deviation (SD) units. Confirmatory factor analysis was used to create DIF-adjusted, cross-nationally harmonized CES-D factor scores.</div></div><div><h3>Results</h3><div>Controlling for underlying depressive symptoms, HCAP participants in India had higher odds of reporting being not hopeful about future (OR = 1.38, 95% confidence interval [CI]: 1.34–1.42), not enjoying life (OR = 1.43, 95% CI: 1.38–1.48), and being unhappy (OR = 1.29, 95% CI: 1.25–1.34), compared to HCAP participants in the United States. These identified DIF items artificially increased mean harmonized CES-D factor scores by 0.48 SD units in the India HCAP, with over 50% of the factor scores increased by over 0.30 SD units, indicating salient DIF in the India HCAP.</div></div><div><h3>Conclusion</h3><div>Our findings demonstrate cross-national heterogeneity in the expression of depressive symptoms. We provide DIF-adjusted CES-D factor scores to improve the quality of cross-national comparisons in aging research.</div></div>","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":"178 ","pages":"Article 111623"},"PeriodicalIF":7.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142792547","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Determinants of cost-effectiveness results of biological therapies for severe asthma: a systematic methodological assessment","authors":"Laura de la Torre-Pérez ,&nbsp;Marilina Santero ,&nbsp;Wendy Nieto-Gutierrez ,&nbsp;Christine Giesen ,&nbsp;Angela Nardin ,&nbsp;Claudia Cosma ,&nbsp;Pedro Silva Pires ,&nbsp;Andrea Guida ,&nbsp;Marcello Simonini ,&nbsp;Camila Quirland Lazo ,&nbsp;Feng Xie ,&nbsp;Pablo Alonso-Coello","doi":"10.1016/j.jclinepi.2024.111621","DOIUrl":"10.1016/j.jclinepi.2024.111621","url":null,"abstract":"<div><h3>Objectives</h3><div>To assess the associations between cost-effectiveness analysis’ (CEA) methodological characteristics and incremental cost-effectiveness ratio outcomes and conclusions, in biological treatments for asthma.</div></div><div><h3>Study Design and Setting</h3><div>We included CEAs comparing biological treatments to standard care, in adults with severe asthma. We performed a search in MEDLINE, EMBASE, and Web of Science (September 2022). We extracted and summarized CEA’s characteristics and critically appraised the studies using the extended Consensus Health Economic Criteria. In those reporting benefits as quality-adjusted life years, we conducted bivariate and regression analyses.</div></div><div><h3>Results</h3><div>We identified 33 CEAs that showed overall good quality (above 66.6% of compliance) with variable results across extended Consensus Health Economic Criteria sections. We included 28 cost-utility analyses on biological treatments in asthma in our analysis. Only industry sponsorship showed significant differences in the bivariate analysis (<em>P</em> = .021 for the difference in incremental cost-effectiveness ratio medians, and <em>P</em> = .027 for the different percentage in reported cost-effectiveness). In the regression adopting a nonlifetime horizon and nonuse of a model (β = 4.25 and β = 0.16, <em>P</em> &lt; .05), significantly associated in the multivariate analysis. Only nonindustry sponsorship showed a significant association with the drug being reported as not cost-effective, both in the bivariate and multivariate analysis (odds ratio = 13.2 and odds ratio = 20.15 <em>P</em> &lt; .05).</div></div><div><h3>Conclusion</h3><div>Our study identified significant limitations, including poor reporting practices and the impact of industry sponsorship on outcomes, with notable effects on cost-effectiveness conclusions. These findings highlight the need for policymakers and health-care decision-makers to meticulously consider methodological rigor and potential biases in economic evaluations.</div></div>","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":"178 ","pages":"Article 111621"},"PeriodicalIF":7.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142787750","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Editors’ Choice: February 2025","authors":"David Tovey,&nbsp;Andrea C. Tricco","doi":"10.1016/j.jclinepi.2025.111681","DOIUrl":"10.1016/j.jclinepi.2025.111681","url":null,"abstract":"","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":"178 ","pages":"Article 111681"},"PeriodicalIF":7.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143030333","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The definition of predictor and outcome variables in mortality prediction models: a scoping review and quality of reporting study","authors":"Eline G.M. Cox ,&nbsp;Daniek A.M. Meijs ,&nbsp;Laure Wynants ,&nbsp;Jan-Willem E.M. Sels ,&nbsp;Jacqueline Koeze ,&nbsp;Frederik Keus ,&nbsp;Bianca Bos - van Dongen ,&nbsp;Iwan C.C. van der Horst ,&nbsp;Bas C.T. van Bussel","doi":"10.1016/j.jclinepi.2024.111605","DOIUrl":"10.1016/j.jclinepi.2024.111605","url":null,"abstract":"<div><h3>Background and Objectives</h3><div>Mortality prediction models are promising tools for guiding clinical decision-making and resource allocation in intensive care units (ICUs). Clearly specified predictor and outcome variables are necessary to enable external validation and safe clinical application of prediction models. The objective of this study was to identify the predictor and outcome variables used in different mortality prediction models in the ICU and investigate their reporting.</div></div><div><h3>Methods</h3><div>For this scoping review, MEDLINE, EMBASE, Web of Science, and the Cochrane Central Register of Controlled Trials were searched. Studies developed within a general ICU population reporting on prediction models with mortality as a primary or secondary outcome were eligible. The selection criteria were adopted from a review by Keuning et al. Predictor and outcome variables, variable characteristics (defined as units, definitions, moments of measurement, and methods of measurement), and publication details (defined as first author, year of publication and title) were extracted from the included studies. Predictor and outcome variable categories were demographics, chronic disease, care logistics, acute diagnosis, clinical examination and physiological derangement, laboratory assessment, additional diagnostics, support and therapy, risk scores, and (mortality) outcomes.</div></div><div><h3>Results</h3><div>A total of 56 mortality prediction models, containing 204 unique predictor and outcome variables, were included. The predictor variables most frequently included in the models were age (40 times), admission type (27 times), and mechanical ventilation (21 times). We observed that single variables were measured with different units, according to different definitions, at a different moment, and with a different method of measurement in different studies. The reporting of the unit was mostly complete (98% overall, 95% in the laboratory assessment category), whereas the definition of the variable (74% overall, 63% in the chronic disease category) and method of measurement (70% overall, 34% in the demographics category) were most often lacking.</div></div><div><h3>Conclusion</h3><div>Accurate and transparent reporting of predictor and outcome variables is paramount to enhance reproducibility, model performance in different contexts, and validity. Since unclarity about the required input data may introduce bias and thereby affect model performance, this study advocates that prognostic ICU models can be improved by transparent and clear reporting of predictor and outcome variables and their characteristics.</div></div>","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":"178 ","pages":"Article 111605"},"PeriodicalIF":7.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142631589","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Key challenges in epidemiology: embracing open science","authors":"Edward Xu ,&nbsp;Anna Catharina V. Armond ,&nbsp;David Moher ,&nbsp;Kelly Cobey","doi":"10.1016/j.jclinepi.2024.111618","DOIUrl":"10.1016/j.jclinepi.2024.111618","url":null,"abstract":"<div><div>Open science is a movement that fosters research transparency, reproducibility, and equity. Open science has been put forward by numerous stakeholders in the research ecosystem as a key science policy goal, with the United Nations Educational, Scientific, and Cultural Organization creating recommendations on open science and aligning these with UN Sustainability Goals. Open science practices are not standard to epidemiology despite their potential value to the field and especially during disease outbreaks. This article highlights core open science practices, including study registration, open data, code, material, use of reporting guideline, open access publishing, and preprints. It aims to provide readers with the fundamentals about open science, relevant international policy for open science, and the value of implementing open science for epidemiology and society as a whole. It is a practical piece that will provide readers with a starting point to expand their understanding of open science and to identify tools to learn more. The article also highlights the challenges of open science in its implementation and the importance of monitoring open science practices.</div></div>","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":"178 ","pages":"Article 111618"},"PeriodicalIF":7.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142752187","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Addressing feature importance biases in machine learning models for early diagnosis of type 1 Gaucher disease","authors":"Yoshiyasu Takefuji","doi":"10.1016/j.jclinepi.2024.111619","DOIUrl":"10.1016/j.jclinepi.2024.111619","url":null,"abstract":"","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":"178 ","pages":"Article 111619"},"PeriodicalIF":7.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142774379","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}