BMC Medical Research Methodology最新文献

{"title":"Real-time prediction of trauma-induced coagulopathy using an inverted transformer (trauma-former): a methodological feasibility and simulation study based on the ADEMP framework.","authors":"Xiaolei Huang, Wenliang Chen, Guan Wei, Wenjia Lin","doi":"10.1186/s12874-026-02909-x","DOIUrl":"10.1186/s12874-026-02909-x","url":null,"abstract":"<p><strong>Background: </strong>Trauma-induced coagulopathy (TIC) is a leading cause of preventable mortality following severe injury, yet prehospital recognition is fundamentally constrained by the limited sensitivity of static vital-sign metrics. Although fifth-generation (5G) wireless technology theoretically enables continuous biosignal transmission, robust computational frameworks for streaming risk prediction remain underdeveloped and largely unvalidated. To address this methodological gap, this study presents Trauma-Former, an inverted Transformer (iTransformer) architecture for real-time TIC prediction, trained and validated on high-fidelity synthetic physiological data within a rigorous in-silico engineering assessment framework.</p><p><strong>Methods: </strong>An Ornstein-Uhlenbeck (OU) process generated 1 Hz vital-sign waveforms (heart rate [HR], systolic and diastolic blood pressure [SBP/DBP], SpO₂) for 1,240 simulated 30-minute transport episodes (50% TIC prevalence), structured according to the ADEMP framework. Trauma-Former embeds 60-second histories as independent variable tokens, applying self-attention across physiological streams to model inter-variable coupling. Models were benchmarked against an expanded set including a linear trend-based logistic regression (LR-trend), a 1D convolutional neural network (1D-CNN), a bidirectional gated recurrent unit (GRU), LSTM, XGBoost, PatchTST, Informer, and the shock index. An independent test set with 25% TIC prevalence assessed performance under clinically representative event rates. A sensitivity analysis incorporating binary missingness indicators probed the missing-completely-at-random (MCAR) assumption. Monte Carlo standard errors (MCSE) quantified simulation uncertainty throughout.</p><p><strong>Results: </strong>On the balanced development set, Trauma-Former achieved an AUROC of 0.939 (95% CI 0.92-0.95; MCSE = 0.003) and an AUPRC of 0.88. Critically, the LR-trend model achieved an AUROC of 0.917 (95% CI 0.89-0.94), confirming that the synthetic task is predominantly solvable by detecting monotonic vital-sign drifts; the incremental contribution of iTransformer's cross-variable attention is consequently modest (AUROC gap: 0.022). Under a realistic 25% prevalence setting, AUROC remained 0.931 but positive predictive value (PPV) collapsed from 0.89 to 0.48-a 46% relative reduction-indicating that fewer than half of all model-generated alerts would correspond to true TIC events in a representative prehospital cohort. Adding binary missingness indicators yielded only a marginal PPV improvement of 0.02 under 30% MCAR data loss. The model demonstrated resilience to 30% packet loss and 4G-equivalent latency jitter with less than 5% AUROC degradation.</p><p><strong>Conclusions: </strong>Trauma-Former provides a reproducible, open-source synthetic testbed for prehospital AI development. All reported metrics represent upper-bound estimates derived under deliberately simplified linear conditi","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":" ","pages":""},"PeriodicalIF":3.7,"publicationDate":"2026-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13495251/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148249053","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":"Application of Q-methodology in chronic disease research: a scoping review.","authors":"Li Zhang, Xue Yang, Guirong Li","doi":"10.1186/s12874-026-02898-x","DOIUrl":"10.1186/s12874-026-02898-x","url":null,"abstract":"<p><strong>Background: </strong>As the global population ages, the burden of chronic disease management continues to intensify. Guided by a patient-centered medical philosophy, there is an urgent need to develop research methods capable of capturing in depth the complex subjective experiences of patients, caregivers, and healthcare providers. This approach aims to address the limitations of current homogeneous intervention strategies. Q-methodology, a research method for exploring the complexities of human subjectivity, demonstrates significant potential across multiple fields, including the management of chronic diseases. This study aims to examine the characteristics of existing Q-methodology research on chronic diseases, including research questions, methodological variations, and implications for research in this field.</p><p><strong>Methods: </strong>This study followed the updated scope review methodology of the Joanna Briggs Institute, as well as the PRISMA-ScR guidelines (Preferred Reporting Items for Systematic Reviews and Scoping Reviews). Literature searches were conducted from inception to November 1, 2025, across four databases: PubMed, Web of Science Core Collection, Embase, and Cochrane Library. The search was limited to English-language publications. Data extraction primarily utilized the Q-methodology evaluation and review tool.</p><p><strong>Results: </strong>Fifteen studies from six countries were included. Participants were individuals with chronic diseases, caregivers, and healthcare providers. Sample sizes ranged from 23 to 60 in the Q Sample and 13 to 136 in the P Sample. Research questions fell into three areas: identifying needs and preferences for support and interventions (N = 9), comparing differences in viewpoints (N = 4), and understanding patients' intrinsic cognition and experiences (N = 2). Each study reported 3 to 8 unique perspectives. Q-methodology use is increasing in chronic disease research; however, methodological variations persist. Notable differences appear in determining the Q sample, selecting P-samples, conducting Q-sorting, and data analysis. Its application in chronic disease studies facilitates the exploration of group perspectives and the comparative analysis of differences in viewpoints, enabling a deeper capture of complex subjective experiences within populations.</p><p><strong>Conclusions: </strong>Preliminary evidence suggests that Q-methodology has clinical utility and scientific rigor in chronic disease research. However, given the limited number of studies and methodological heterogeneity, more research is needed to confirm these findings. Future efforts should focus on developing more scientifically comprehensive reporting checklists to standardize their use.</p><p><strong>Trial registration: </strong>As the research did not directly involve animals or humans, no ethical review was conducted.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":" ","pages":""},"PeriodicalIF":3.7,"publicationDate":"2026-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13374176/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148238946","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":"Mitigating bias in the analysis and inferences from using longitudinal EHR data in disease outcomes research.","authors":"Cassandra Hennessy, Alison Z Swartz, Frank E Harrell, Heidi J Silver","doi":"10.1186/s12874-026-02881-6","DOIUrl":"10.1186/s12874-026-02881-6","url":null,"abstract":"<p><strong>Background: </strong>The electronic health record (EHR) provides an opportunity for extracting a wealth of up-to-date real-world longitudinal data. Although the main limitations of using EHR data have been well-recognized and well-described, under-recognized factors may threaten the reliability of inferences made regarding the impact of EHR variables on chronic disease outcomes. A problem not well-recognized is the impact of routinely acquired variables that are documented with every patient encounter regardless of the reason for the encounter such as vital signs, height, and body weight.</p><p><strong>Methods: </strong>We utilized a landmark approach to identify occurrence of 10 cardiovascular-related disease outcomes after a 5-year observation period during which all body weights recorded in the EHR were used in multivariate cox regression modeling to identify the strongest of 9 weight-based predictor variables for each of the 10 disease outcomes.</p><p><strong>Results: </strong>We found that the number of recorded weights, as an independent variable, was the strongest predictor for all 10 cardiovascular-related disease outcomes when compared to all other weight-based variables (lowest weight, highest weight, average weight, last weight, absolute weight change, maximum weight change, weight fluctuation, and weight cycling) as well as BMI. The findings demonstrate the importance of recognizing and accounting for the number of times a more frequently measured clinical variable, such as body weight, is recorded as it is critical to determine the true impact of other similar variables on disease outcomes when conducting longitudinal analysis of EHR data.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":" ","pages":""},"PeriodicalIF":3.7,"publicationDate":"2026-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13480091/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148223167","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":"Predictive averaging and Rubin's rule-based model pooling to predict survival risk with imputations in the presence of missing patient data: methodology and verification using two case studies and simulations.","authors":"Bart J A Mertens, Lars L J van der Burg, Erwan Donal, Nina Ajmone Marsan, Liesbeth C de Wreede","doi":"10.1186/s12874-026-02888-z","DOIUrl":"10.1186/s12874-026-02888-z","url":null,"abstract":"<p><strong>Background: </strong>Practical estimation of patient risk is often complicated by predictor values being unobserved or missing. In this paper we study the calibration and application of models for patient risk prediction when both the past patient data to which the prediction models are fit contains such missing values, while the future patient records may also be partially observed. Typical applications are often encountered in prognostic observational research or with registry data.</p><p><strong>Methods: </strong>This paper presents a detailed study on the use of multiple imputation in prediction, to deal with missing values in both past (calibration data) as well as future patient predictor records. We present the two distinct approaches which may be considered to use imputations to account for the presence of missing values. One method directly averages survival predictions obtained from separate models which are fitted on distinct imputations of the data. The other method first pools the intermediate effect and baseline estimates of these models, before calculation of the predictions from the pooled model. Such pooled estimates could be obtained from Rubin's rules, for example. Methods are introduced and demonstrated based on two motivating datasets. The application for Cox regression survival modeling is studied in detail. Method performance is verified through cross-validation, a separate validation set and an extensive simulation study.</p><p><strong>Results: </strong>All methods are comparable with respect to bias and Brier score (accuracy) assessment. Major differences are however found between both methods when comparing predicted per-patient risks between repetitions of the fitting procedure with a different set of imputations and when the same number of imputations is used. Predictive averaging is preferable, because the difference between such replicate predictions can be reduced to zero by increasing the number of imputations, which is not the case with the pooled model.</p><p><strong>Conclusions: </strong>Predictive averaging should be preferred to use of a single pooled model, when using multiple imputations for missing predictor data. Single imputation should not be used in prediction on missing data.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":" ","pages":""},"PeriodicalIF":3.7,"publicationDate":"2026-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13474813/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148216447","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":"Experiences from conducting systematic reviews of systematic reviews.","authors":"Wilhelmus Johannes Andreas Grooten, Tony Bohman, Carina Bostrom, Åsa Dedering, Marie Halvorsen, Roman P Kuster, Lena Nilsson-Wikmar, Christina B Olsson, Graciela Rovner, Elena Tseli, Eva Rasmussen-Barr","doi":"10.1186/s12874-026-02880-7","DOIUrl":"10.1186/s12874-026-02880-7","url":null,"abstract":"<p><p>This commentary discusses recent experiences from conducting two systematic reviews (SRs) of SRs. We address the strengths and limitations of conducting an SR of SRs in the field of evidence-based medicine in musculoskeletal health. Despite methodological challenges, we believe that SRs of SRs add value to the research community.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"26 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2026-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13251232/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148209653","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":"Enhancing recruitment methodologies: leveraging the Tailored Design Method to survey populations with varied engagement in healthcare.","authors":"Hari H Venkatachalam, Nina A Sayer, Heather Belanger, Peter A Toyinbo, Stephen L Luther, Shannon R Miles","doi":"10.1186/s12874-026-02895-0","DOIUrl":"10.1186/s12874-026-02895-0","url":null,"abstract":"<p><strong>Background: </strong>Successfully recruiting respondents to complete surveys is integral for ensuring representative samples and providing adequate power for analyses. However, obtaining survey responses for public health and health services research can be challenging, especially when working with populations with varied engagement with the providers of the institution conducting the study. Recruitment may be particularly challenging when selecting potential participants based on historical medical encounters that they may not remember, such as a brief health screen. Many researchers implement the Tailored Design Method (TDM) to optimize response rates. Adaptations to TDM, however, may be needed for researchers in federal institutions conducting surveys of populations with varied levels of healthcare engagement. This manuscript describes TDM implementation techniques and modifications that comply with federal regulations, and factors associated with response rates and survey response type.</p><p><strong>Methods: </strong>We surveyed a random, stratified sample of 9,775 U.S. Iraq and Afghanistan war veterans whom the Veterans Health Administration (VHA) had screened between 2007 and 2018 for post-deployment health concerns. We implemented a modified TDM that complied with federal regulations to survey veterans by email and USPS between July 2020 and August 2021.</p><p><strong>Results: </strong>Twenty-one percent (N = 2,025) responded. It took a median of 21 and 69 days to receive responses via electronic survey and paper booklets, respectively. The random sample of veterans had varied levels of engagement with the VHA at the time of health screening and survey. Respondents and non-respondents used equal amounts of neurology, physical medicine and rehabilitation, and mental health VHA services in the 6 months post-screens. At the time of the survey (an average of 9 years post-screen), 21% of respondents reported not having recently used VHA services. Respondents were more likely to be older, White, and female than non-respondents, although differences were small in magnitude. Compared to electronic survey respondents, paper booklet respondents were more likely to be older, rural, and screened earlier, and less likely to live in the same state as the research team. When controlling for first contact mode (email vs. USPS), only age predicted whether veterans responded by paper booklet or electronic survey.</p><p><strong>Conclusions: </strong>Investigators working in federal organizations should be aware of possible lower response rates when compared to surveys of civilians. Researchers in federal institutions seeking to optimize response rates may find our TDM adaptations useful when surveying historical cohorts or populations without regular interaction with the research institution. Our response rate (~ 21%) may be useful for researchers conducting sample size calculations with similar populations.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":" ","pages":""},"PeriodicalIF":3.7,"publicationDate":"2026-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13470937/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148203667","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 longitudinal multivariate trajectories using an ensemble of principal component trees.","authors":"Bastian Pfeifer, Simon Grabner, Andrea Berghold, Markus Loecher","doi":"10.1186/s12874-026-02903-3","DOIUrl":"10.1186/s12874-026-02903-3","url":null,"abstract":"<p><strong>Purpose: </strong>Clustering longitudinal data is challenging, particularly when measurements are high-dimensional, irregularly sampled, or noisy. We aim to provide a flexible and interpretable framework for identifying meaningful temporal subgroups and their key features.</p><p><strong>Methods: </strong>We introduce TAPIO, an ensemble-based clustering approach with longitudinal extensions, including longTAPIO_trajectories, longTAPIO_sample, and longTAPIO_MLD. These variants integrate dimension reduction and cluster-specific feature importance, allowing robust clustering of univariate and multivariate trajectories, as well as regularly and irregularly sampled longitudinal data.</p><p><strong>Results: </strong>Simulation studies demonstrate that TAPIO accurately recovers cluster structure, identifies relevant features, and performs competitively with existing methods. longTAPIO_trajectories excels on regularly sampled data, while longTAPIO_MLD outperforms alternatives for irregular measurements. Applications on a clinical cohort reveal patient subgroups with distinct survival patterns driven by key cardiac measures, and analyses of high-dimensional longitudinal proteomics data uncover molecularly distinct clusters with interpretable protein-level importance profiles.</p><p><strong>Conclusion: </strong>TAPIO offers a scalable, interpretable framework for longitudinal clustering that accommodates complex multivariate trajectories and high-dimensional data. Its ability to identify both meaningful clusters and their defining features has potential to advance patient stratification, biomarker discovery, and longitudinal data analysis in biomedical research.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":" ","pages":""},"PeriodicalIF":3.7,"publicationDate":"2026-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13474914/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148209601","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":"Reducing unmeasured confounding from non-compliance with randomization in surgical RCTs: a simulation study.","authors":"Zhenzhen Lu, Wenwen Li, Mengyao Wang, Lihong Huang, Feng Chen","doi":"10.1186/s12874-026-02897-y","DOIUrl":"10.1186/s12874-026-02897-y","url":null,"abstract":"<p><strong>Background: </strong>Non-compliance with randomization is pervasive in surgical randomized controlled trials (RCTs), introducing unmeasured confounding that undermines valid causal inference. Conventional methods, such as intention-to-treat (ITT), per-protocol (PP), as-treated (AT) and instrumental variable (IV) analysis, exhibit significant limitations.</p><p><strong>Methods: </strong>We conducted a simulation study to compare the performance of negative control methods (NCM), including difference-in-differences (DiD) and double negative controls (DNC), with traditional approaches (ITT, PP, AT, IV). Parameters from influential lumbar discectomy trials informed realistic crossover scenarios with crossover rates ranging from 10% to 40%. Performance was assessed via bias, mean squared error (MSE), and coverage probability (CP) for the method-specific treatment effect estimator across varying sample sizes (n = 100-500) and confounding structures.</p><p><strong>Results: </strong>ITT, PP, and AT estimators showed severe bias, with CP ≤ 0.1% across the evaluated scenarios. The IV estimator showed small average bias but high estimation variability (MSE = 25.612 at a crossover rate of 40%). The DiD estimator showed low bias and near-nominal coverage under equal confounding (|bias| ≤ 0.003, CP ≥ 94.3%) but showed poor performance when this assumption was violated. In contrast, the DNC estimator remained stable, with |bias| ≤ 0.415, MSE ≤ 2.515, and near-nominal coverage across the evaluated parameter combinations. At n = 100 with a crossover rate of 40%, DNC reduced MSE by > 90% relative to IV while maintaining CP = 97.7%.</p><p><strong>Conclusions: </strong>The DNC approach effectively mitigates unmeasured confounding arising from non-compliance with randomization in surgical RCTs, offering superior accuracy and reliability compared to conventional methods.</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/PMC13459754/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148175435","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":"Dynamic prediction of adverse outcomes in discharged COPD patients using patient-reported outcomes.","authors":"Jie Jin, Hangzhi He, Xiaojuan Hu, Shuping Xie, Xiangli Lu, Hui Zhao, Yanbo Zhang","doi":"10.1186/s12874-026-02889-y","DOIUrl":"10.1186/s12874-026-02889-y","url":null,"abstract":"<p><strong>Background: </strong>To develop dynamic prediction models based on longitudinal patient-reported outcomes (PROs) and evaluate the time-varying risk of adverse outcomes after hospital discharge in patients with chronic obstructive pulmonary disease (COPD), thereby improving the accuracy of risk prediction.</p><p><strong>Methods: </strong>Hospitalized COPD patients who met the inclusion and exclusion criteria from five hospitals in Shanxi Province between December 2020 and April 2022 were included. The occurrence of readmission or death within three years after discharge was defined as the adverse outcome. PROs measured repeatedly during follow-up were treated as time-varying predictors. Three dynamic prediction approaches were constructed and compared: the Joint Latent Class Model (JLCM), Shared Random-Effects Model (SREM), and Landmark model. Model performance was evaluated and compared using the time-dependent area under the receiver operating characteristic curve (AUC), Brier score, and calibration curves. In addition, individual-level trajectories of PRO scores and their corresponding dynamic risk predictions were illustrated.</p><p><strong>Results: </strong>A total of 344 COPD patients were included, among whom 111 patients (32.27%) experienced adverse outcomes during follow-up. Model comparison demonstrated that the SREM model consistently achieved higher AUC values and lower Brier scores across all landmark time points, and its calibration curves were close to the ideal diagonal line, indicating the best predictive performance. In the survival submodel, the association parameter for PRO scores was statistically significant (P < 0.001). Each one-unit increase in PRO score was associated with a 4.3% reduction in the risk of adverse outcomes (HR = 0.957, 95% CI: 0.933-0.980). In addition, educational level, procalcitonin, and C-reactive protein levels were significantly associated with the risk of adverse outcomes. In the longitudinal submodel, dyspnea severity (mMRC grade) was significantly negatively associated with PRO scores.</p><p><strong>Conclusions: </strong>Longitudinal PRO data effectively capture the dynamic changes in health status among COPD patients and can serve as important time-varying predictors for adverse outcomes. Among the three dynamic prediction approaches evaluated, the SREM model demonstrated the best predictive performance. These findings provide a scientific basis for risk stratification and individualized management strategies in COPD patients after hospital discharge.</p><p><strong>Clinical trial registration: </strong>The study was registered with the Chinese Clinical Trial Registry (Registration number: ChiCTR2200064900;Date of Registration:2022-10-21).</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/PMC13465670/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148161891","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":"EQUATOR reporting guideline use and editorial enforcement in four medical specialties: a cross-sectional study of 20 high-impact journals.","authors":"Antonio Cisneros-Barrios, Pamela Hernández-Delgado, Paloma Gradilla-Magaña, Ismael Gutierrez-Jimenez, Cesar Ramos-Remus","doi":"10.1186/s12874-026-02901-5","DOIUrl":"10.1186/s12874-026-02901-5","url":null,"abstract":"<p><strong>Background: </strong>EQUATOR (Enhancing the QUAlity and Transparency Of Health Research) reporting guidelines aim to improve transparency and completeness in biomedical research; however, their adoption remains inconsistent across medical specialties. We quantified declared reporting guideline use in high-impact ophthalmology journals and assessed whether editorial policies are associated with their implementation across specialties.</p><p><strong>Methods: </strong>We conducted a cross-sectional study of original research articles published in 2024 across five first-quartile ophthalmology journals. Reporting guideline use was identified through systematic full-text searches. An \"advisable use rate\" was defined as the proportion of articles declaring guideline use among those for which use was methodologically appropriate. Journal enforcement policies were characterized through analysis of instructions for authors. Guideline use in ophthalmology was contextualized through comparison with previously published datasets from orthopedics, rheumatology, and infectious diseases.</p><p><strong>Results: </strong>Among 794 original research articles, reporting guidelines were deemed advisable for 672 (84.6%). Of these, 192 articles (28.6%) declared guideline use. Advisable use rates ranged from 5% to 120% across journals. STROBE was the most frequently applicable guideline but was declared in only 27% of eligible articles. Ophthalmology guideline use exceeded orthopedics (10.2%), rheumatology (7.2%), and infectious diseases (6.3%). Although enforcement policies varied across journals, no significant correlation was observed between enforcement level and guideline use (Kendall's τ = 0.24, p > 0.05).</p><p><strong>Conclusions: </strong>Despite higher adoption relative to other specialties, reporting guideline use in ophthalmology remains limited. Substantial inter-journal variability and the absence of an association between enforcement policies and use highlight opportunities to better align editorial expectations with reporting practices.</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/PMC13459384/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148175463","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}