BMC Medical Research Methodology最新文献

{"title":"Agreement between ChatGPT and human-derived multilevel meta-analyses: a reproducibility study across clinical evidence syntheses.","authors":"Nikolai Ramadanov, Hannah Jaeger, Mikhail Salzmann, Robert Hable","doi":"10.1186/s12874-026-02879-0","DOIUrl":"10.1186/s12874-026-02879-0","url":null,"abstract":"<p><strong>Background: </strong>Large language models are increasingly integrated into biomedical research workflows, yet their ability to reliably reproduce complex statistical analyses remains insufficiently evaluated. While prior studies suggest that ChatGPT can replicate conventional meta-analyses, its performance in recalculating statistically demanding multilevel meta-analyses is unknown.</p><p><strong>Methods: </strong>We conducted a systematic review to identify published multilevel meta-analyses (i.e., models accounting for dependency among multiple effect sizes within studies) of hip-related clinical studies that provided extraction tables enabling independent recalculation. For each outcome, pooled estimates and heterogeneity parameters were recomputed using ChatGPT under predefined analytical assumptions and compared with statistician-derived results. Agreement was assessed using absolute and relative differences and Bland-Altman analyses for pooled effects, and qualitative comparison of heterogeneity measures (I², τ²) based on absolute differences.</p><p><strong>Results: </strong>Seven studies, each reporting a multilevel meta-analysis, comprising more than 40 pooled effect estimates were included. ChatGPT-derived pooled estimates showed close numerical agreement with human-derived results, with small median absolute differences for continuous (2.25) and binary outcomes (0.005) and minimal overall bias. Heterogeneity estimates demonstrated similar concordance, with predominantly small differences that did not alter qualitative interpretation. Larger deviations were limited to a small number of outcomes with extreme heterogeneity or inconsistent input structures.</p><p><strong>Conclusion: </strong>ChatGPT can reproducibly approximate the results of multilevel meta-analyses under predefined analytical conditions. These findings support the potential role of large language models as adjunct tools for reproducibility assessment and methodological validation in evidence synthesis, while emphasizing the continued need for expert oversight.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":" ","pages":""},"PeriodicalIF":3.7,"publicationDate":"2026-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13621747/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148410016","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The survival double descent: generalization dynamics of deep neural networks in time-to-event analysis.","authors":"Steven N Hart, Ann L Oberg","doi":"10.1186/s12874-026-02894-1","DOIUrl":"10.1186/s12874-026-02894-1","url":null,"abstract":"<p><p>Recent work on double descent has challenged classical bias-variance tradeoffs, showing that test error can decrease, increase sharply near the interpolation threshold, and then decrease again as model capacity grows. This phenomenon has been documented in regression and classification, but its relevance to survival analysis remains unclear. Survival data are subject to censoring, which obscures true event times, and widely used models such as the Cox proportional hazards model are optimized via partial likelihoods that emphasize ranking rather than calibrated risk estimation. It is therefore unknown whether double descent occurs in this setting, how censoring influences its manifestation, or how it interacts with standard performance metrics.We investigate these questions using synthetic survival data generated from Weibull hazards with controlled censoring, allowing systematic variation of model capacity from under to over parameterized regimes.While we verified double descent occurs in survival models, calibration plateaus and decouples from discrimination, even under strong [Formula: see text] regularization. This decoupling arises because the Cox partial likelihood optimizes rankings rather than magnitudes, producing extreme risk scores that break the Breslow estimator used to estimate survival probabilities. Validation on two real-world clinical datasets, the METABRIC breast cancer cohort and the SUPPORT study of seriously ill hospitalized adults, confirms the calibration-discrimination decoupling: IBS saturates at a constant value as network width grows while concordance varies, reproducing the primary synthetic finding across distinct clinical domains and sample sizes. These results highlight limitations of discrimination-based model selection in survival analysis and underscore the need for calibration-aware evaluation in high-capacity prognostic models.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":" ","pages":""},"PeriodicalIF":3.7,"publicationDate":"2026-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13465384/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148155346","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Capacity building at an Australian regional clinical trials unit: determinants of growth and correlation with a capability framework.","authors":"Lisa A Bell, Rebecca Gurnett, Carmel E Goss, Geoffrey Chong, Ethan L Pitman, Michael Tuohill, Ashleigh Clarke, Virginia Murphy, Wasek Faisal","doi":"10.1186/s12874-026-02902-4","DOIUrl":"10.1186/s12874-026-02902-4","url":null,"abstract":"<p><strong>Background: </strong>Expanding the capacity for clinical trials in regional/nonmetropolitan locations will increase accessibility for patients who live remotely, however there are limited studies on the growth of regional clinical trials units over time. This study aims to explore the status of the growth of a regional Australian clinical trial unit between 2011 and 2022, utilising a recently published clinical trial capability framework.</p><p><strong>Methods: </strong>An evaluation of a regional clinical trial unit in Victoria, Australia, between 2011 and 2022 was conducted to objectively describe growth, including the number of trials, number of patients and number of principal investigators (PIs). We used an objective capability framework published by Woollett et al. (2023). The framework uses a maturity model applied across several domains. Descriptive statistics are reported as totals and means. Scatter plots, pairwise correlations and Bland-Altman adjustments for trends were computed to show correlations and agreements between the framework average score and objective measures of the clinical trials between 2011 and 2022.</p><p><strong>Results: </strong>The number of clinical trials active in 2011 and 2022 were n = 4 and n = 70, respectively. Across all domains, the Grampians Health Clinical Trials Unit was assessed as 'established' in 2011, whereas the trial unit was considered 'high performing' in 2022. The framework capability score correlated well with the number of PIs per year (r (10) = 0.9, p < 0.001), the number of open trials per year (r (10) = 0.9, p < 0.001), and the number of patients (r (10) = 0.8, p < 0.001). The key contributors to growth identified were organisational support and infrastructure. Growth was largely driven by oncology trials.</p><p><strong>Conclusion: </strong>Significant growth was observed for a regional clinical trial unit across a decade. The framework score strongly correlated with objective clinical trial data. Understanding the growth of clinical trial units in nonmetropolitan areas is essential for developing the capacity of clinical trials in regional and rural areas.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":" ","pages":""},"PeriodicalIF":3.7,"publicationDate":"2026-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13450529/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148161856","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatially varying coefficient generalized distributed lag models for binary response with MCEM estimation.","authors":"Ali Hadianfar, Omid Karimi, Azadeh Saki","doi":"10.1186/s12874-026-02886-1","DOIUrl":"10.1186/s12874-026-02886-1","url":null,"abstract":"<p><p>The adverse health effects of environmental exposures often manifest after a time delay. Distributed lag models (DLMs) are well-suited for modeling these delayed effects and play a central role in identifying critical exposure windows in environmental epidemiology. However, traditional DLMs frequently fail to account for potential spatial dependence and spatial non-stationarity, limiting their accuracy and generalizability across diverse geographical contexts. To address this gap, we propose a spatially varying coefficient generalized distributed lag model (SVCGDLM) that integrates spatial heterogeneity directly into the DLM framework. To estimate model parameters, we develop an efficient Monte Carlo Expectation-Maximization (MCEM) algorithm employing Pólya-Gamma data augmentation and Gibbs sampling to approximate the conditional expectations in the E-step. Through a comprehensive simulation study, we show that the proposed model significantly outperforms standard generalized linear models (GLMs), generalized geographically weighted regression (GGWR), and Generalized Linear Mixed Model using the INLA Approximation (GLMM-INLA) in terms of parameter estimation accuracy and predictive power. Finally, we apply our model to a real-world dataset from Mashhad, Iran, to estimate the spatially varying association between short-term exposure to PM_2.5 and cardiovascular mortality.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":" ","pages":""},"PeriodicalIF":3.7,"publicationDate":"2026-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13411993/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148148275","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The GALENOS approach to triangulating evidence: a structured approach for integrating information from human and animal studies.","authors":"Thomy Tonia, Julian P T Higgins, Virginia Chiocchia, James Downs, Emily Wheeler, Jennifer Potts, Katharine A Smith, Malcolm Macleod, Gin S Malhi, Andrea Cipriani, Georgia Salanti","doi":"10.1186/s12874-026-02891-4","DOIUrl":"10.1186/s12874-026-02891-4","url":null,"abstract":"<p><strong>Background: </strong>Most decision-making research involves collating multiple types of evidence, and such integration should be a core component of investigative practice in science. Triangulation is one formal approach that has proven effective, typically drawing on sources of evidence with different biases. However, there remains no standardised method for its implementation and no clear guidance on how to apply triangulation in research contexts-particularly when synthesising evidence across human and animal data.</p><p><strong>Methods: </strong>GALENOS (Global Alliance for Living Evidence on aNxiety, depressiOn and pSychosis) has introduced GATE (GALENOS Approach to Triangulating Evidence), a multifaceted framework for such triangulation. To further develop GATE, we here systematically searched the literature for work describing triangulation processes for human and animal data.</p><p><strong>Results: </strong>As a key component of GATE, we provide conceptual details and practical guidance on how to prepare, organise, conduct, and follow up on triangulation meetings.</p><p><strong>Conclusions: </strong>Based on findings from this literature and our experience with GATE, we updated our approach.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":" ","pages":""},"PeriodicalIF":3.7,"publicationDate":"2026-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13445838/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148148288","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A comparative simulation study of cluster ensemble algorithms integrated with multiple imputation for clustering with missing data.","authors":"Yui Tomo, Funato Sato, Mari Oba","doi":"10.1186/s12874-026-02869-2","DOIUrl":"10.1186/s12874-026-02869-2","url":null,"abstract":"<p><p>Since cluster analysis methods usually cannot be applied directly to data with missing values, various approaches have been investigated to handle the problem. Multiple imputation is one of the standard procedures for addressing the problem of missing data. In cluster analysis, instead of Rubin's rule, cluster ensemble methods have been proposed to be combined with multiple imputation. However, it remains unrevealed which of the cluster ensemble algorithms leads to better performance when integrated with the procedure. Therefore, we conducted numerical comparisons of several algorithms to integrate the results from k-means++ clustering for multiply imputed datasets and also applied the combined approaches to two real datasets. Our results suggest that the non-negative matrix factorization algorithm may be suitable for scenarios with class balance, whereas the greedy and agglomerative cluster algorithms may be suitable for scenarios with class imbalance. Before application to actual datasets, we still recommend performing simulation experiments in scenarios reflecting the characteristics of the datasets and the assumption of missing value mechanisms.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":" ","pages":""},"PeriodicalIF":3.7,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13439842/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148142657","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A staged Bayesian framework for longitudinal prediction of adolescent depression using add health data.","authors":"Niloofar Ramezani, Ehiremen Adesua Azugbene, Jeffrey R Wilson","doi":"10.1186/s12874-026-02890-5","DOIUrl":"10.1186/s12874-026-02890-5","url":null,"abstract":"<p><strong>Background: </strong>Accurately predicting adolescent depression from longitudinal behavioral data requires methods that incorporate feedback between prior mental health and substance use while remaining reproducible and interpretable. The objective of this study is predictive rather than explanatory or causal.</p><p><strong>Methods: </strong>Using Waves I-IV of the U.S. National Longitudinal Study of Adolescent to Adult Health (Add Health), we developed a novel staged Bayesian framework for longitudinal prediction: (Stage 1) multivariate logistic models with correlated random intercepts for depression, smoking, and alcohol use; (Stage 2) a composite substance-use indicator jointly modeled with depression; and (Stage 3) a temporal feedback model including lagged depression and lagged substance use. Posterior predictive checks and internal validation were used to assess model adequacy. Discrimination (area under the receiver operating characteristic curve [AUC]) and calibration (Brier score) were summarized across stages. The models are predictive rather than explanatory and are not intended to estimate causal effects.</p><p><strong>Results: </strong>Lagged depression (posterior mean = 0.98; 95% highest posterior density [HPD] interval: 0.45-1.52) and lagged substance use (posterior mean = 0.69; 95% HPD: 0.12-1.25) were associated with increased odds of future depression. Predictive performance improved from Stage 1 to Stage 3: AUC increased from 0.78 to 0.84, and Brier score decreased from 0.185 to 0.152. Posterior predictive checks supported model adequacy.</p><p><strong>Conclusions: </strong>The staged Bayesian pipeline provides a generalizable template for longitudinal prediction with temporal feedback, improving discrimination and calibration while preserving interpretability.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":" ","pages":""},"PeriodicalIF":3.7,"publicationDate":"2026-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13371721/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148052566","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating Elicit's systematic reviews workflow in an umbrella review on air pollution and acute lower respiratory infections: a methodological study for quality appraisal.","authors":"Cristina Mazzali, Tiziana Pinciroli, Maria Rosa Valetto, Pietro Dri, Antonio Giampiero Russo","doi":"10.1186/s12874-026-02892-3","DOIUrl":"10.1186/s12874-026-02892-3","url":null,"abstract":"<p><strong>Background: </strong>The exponential growth of scientific publications has increased the complexity of evidence synthesis. Systematic reviews remain essential but highly resource-intensive. Large language models (LLMs) offer new opportunities to support or partially automate key steps of this process. This study evaluates the performance of Elicit's Systematic Reviews workflow in comparison to the traditional methodology, using as reference a published umbrella review on the association between air pollution and acute lower respiratory infections (ALRI).</p><p><strong>Methods: </strong>A parallel workflow was developed to reproduce each phase of the traditional review. Considering article retrieval, for the traditional workflow, articles were retrieved through a Boolean search, while for the AI-assisted workflow a natural-language query submitted to Elicit. Screening was conducted in two steps, emulating the traditional PECOS-based criteria. Full-text evaluation and quality appraisal were performed through Elicit's \"data extraction\" functionalities. For quality appraisal the validated AMSTAR-2 EH questionnaire was applied.</p><p><strong>Results: </strong>The traditional Boolean search identified 324 unique articles. When compared with the 500 records retrieved by Elicit, an overlap of 8% was observed, which prevented a direct, recall-oriented comparison of search performance. To enable a controlled comparison of downstream steps, we applied Elicit's screening and data-extraction functions to the 324 records identified through the Boolean search, using empirically defined screening-score threshold in Elicit to select studies for further evaluation. In the screening on title and abstract, 33 articles were identified through the traditional workflow and 70 through Elicit, 30 articles overlapping (recall 90.9%, precision 42.9%). The full-text screening selected 15 articles with the traditional methodology and 24 with Elicit, all 15 articles from the traditional methodology being included in Elicit selection (recall 100%, precision 62.5%). In the quality assessment, Elicit showed 24.4% disagreement on general items and 30.4% on additional items of the AMSTAR-2 EH. Errors clustered around multi-component questions, items requiring expert interpretation, and information located in supplementary materials.</p><p><strong>Conclusions: </strong>Elicit can support several phases of systematic reviews and reduce manual workload, but it cannot independently reproduce the methodological rigor required for high-quality evidence synthesis. At present, LLM-based tools are best positioned as complementary systems within human-supervised workflows.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":" ","pages":""},"PeriodicalIF":3.7,"publicationDate":"2026-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13412304/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148101076","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Clustering methods for categorical time series and sequences : a scoping review.","authors":"Ottavio Khalifa, Alan Balendran, Viet-Thi Tran, François Petit","doi":"10.1186/s12874-026-02857-6","DOIUrl":"10.1186/s12874-026-02857-6","url":null,"abstract":"<p><strong>Objective: </strong>To provide an overview of clustering methods for categorical time series (CTS), a data structure common in epidemiology, sociology, biology, and marketing, and to support method selection according to data characteristics.</p><p><strong>Materials and methods: </strong>We searched PubMed (via MEDLINE), Web of Science, and Google Scholar up to November 2024 for articles proposing and evaluating CTS clustering techniques. Methods were classified into three families-distance-based, feature-based, and model-based-and assessed for their ability to address challenges such as variable sequence length, multivariate data, continuous time, missing data, covariates, and large data volumes.</p><p><strong>Results: </strong>Of 14,607 records retrieved, 124 articles describing 129 methods were included. Distance-based approaches, especially those using Optimal Matching, were most common, with 56 methods. We found 28 model-based methods, which covered a broader range of complex data structures such as multivariate data, continuous time and time-invariant covariates. We recorded 45 feature-based approaches, which were on average more scalable but less flexible. Fewer than half of the methods provided public implementations. A searchable Web application ( https://cts-clustering-scoping-review-7sxqj3sameqvmwkvnzfynz.streamlit.app/ ) was developed to support method selection.</p><p><strong>Discussion: </strong>CTS clustering methods are highly heterogeneous in assumptions, capabilities, and scalability. Distance-based approaches dominate, but model-based methods offer richer modeling potential, while feature-based ones emphasize performance at the cost of flexibility.</p><p><strong>Conclusion: </strong>This review highlights methodological diversity and gaps in CTS clustering. The proposed typology and Web application aim to help researchers choose appropriate methods to choose appropriate methods for their data.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"26 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2026-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13217966/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148052551","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Translating treatment effects between correlated endpoints.","authors":"Nusrat Rabbee","doi":"10.1186/s12874-026-02852-x","DOIUrl":"10.1186/s12874-026-02852-x","url":null,"abstract":"<p><p>Clinical researchers frequently need to answer: 'If a treatment changes endpoint 1 by X%, what change in endpoint 2 should we expect?' Current meta-analytic approaches either produce biased estimates (Daniels-Hughes) or provide correlation measures without explicit translational coefficients. We demonstrate how multivariate random-effects meta-analysis can derive the Expected Translational Association (η), which translates treatment effects across correlated endpoints, simultaneously fitting all correlated endpoints through their shared variance-covariance structure, yielding unbiased estimates and nominal confidence coverage. For convenience, we refer to this stacked implementation as SLIM (Stacked LInear Mixed Effects Model). Extensive simulations demonstrated that SLIM maintains near-zero bias and nominal coverage across scenarios, while the Daniels-Hughes approach exhibits substantial bias due to measurement-error-in-covariates, with bias persisting as sample size increases. Using the abdominal aortic aneurysm (AAA) dataset, we demonstrate how this approach quantifies translational associations between growth-rate and rupture-risk endpoints and enables quantitative biomarker-outcome \"what if\" analyses, while showing that Bayesian meta-regression approaches can underestimate risk at clinically critical ranges. The framework generalizes naturally to multivariate systems, offering a rigorous and transparent foundation for evidence synthesis in translational science.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"26 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2026-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13220494/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148052585","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}