BMC Medical Research Methodology最新文献

{"title":"An automated approach to predict diabetic patients using KNN imputation and effective data mining techniques.","authors":"Abdulaziz Altamimi, Aisha Ahmed Alarfaj, Muhammad Umer, Ebtisam Abdullah Alabdulqader, Shtwai Alsubai, Tai-Hoon Kim, Imran Ashraf","doi":"10.1186/s12874-024-02324-0","DOIUrl":"https://doi.org/10.1186/s12874-024-02324-0","url":null,"abstract":"<p><p>Diabetes is thought to be the most common illness in underdeveloped nations. Early detection and competent medical care are crucial steps in reducing the effects of diabetes. Examining the signs associated with diabetes is one of the most effective ways to identify the condition. The problem of missing data is not very well investigated in existing works. In addition, existing studies on diabetes detection lack accuracy and robustness. The available datasets frequently contain missing information for the automated detection of diabetes, which might negatively impact machine learning model performance. This work suggests an automated diabetes prediction method that achieves high accuracy and effectively manages missing variables in order to address this problem. The proposed strategy employs a stacked ensemble voting classifier model with three machine learning models. and a KNN Imputer to handle missing values. Using the KNN imputer, the suggested model performs exceptionally well, with accuracy, precision, recall, F1 score, and MCC of 98.59%, 99.26%, 99.75%, 99.45%, and 99.24%, respectively. In two scenarios one with missing values eliminated and the other with KNN imputer, the study thoroughly compared the suggested model with seven other machine learning techniques. The outcomes demonstrate the superiority of the suggested model over current state-of-the-art methods and confirm its efficacy. This work demonstrates the capability of KNN imputer and looks at the problem of missing values for diabetes detection. Medical professionals can utilize the results to improve care for diabetes patients and discover problems early.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"24 1","pages":"221"},"PeriodicalIF":3.9,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11438170/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142341472","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":"Predicting diabetes in adults: identifying important features in unbalanced data over a 5-year cohort study using machine learning algorithm.","authors":"Maryam Talebi Moghaddam, Yones Jahani, Zahra Arefzadeh, Azizallah Dehghan, Mohsen Khaleghi, Mehdi Sharafi, Ghasem Nikfar","doi":"10.1186/s12874-024-02341-z","DOIUrl":"https://doi.org/10.1186/s12874-024-02341-z","url":null,"abstract":"<p><strong>Background: </strong>Imbalanced datasets pose significant challenges in predictive modeling, leading to biased outcomes and reduced model reliability. This study addresses data imbalance in diabetes prediction using machine learning techniques. Utilizing data from the Fasa Adult Cohort Study (FACS) with a 5-year follow-up of 10,000 participants, we developed predictive models for Type 2 diabetes.</p><p><strong>Methods: </strong>We employed various data-level and algorithm-level interventions, including SMOTE, ADASYN, SMOTEENN, Random Over Sampling and KMeansSMOTE, paired with Random Forest, Gradient Boosting, Decision Tree and Multi-Layer Perceptron (MLP) classifier. We evaluated model performance using F1 score, AUC, and G-means-metrics chosen to provide a comprehensive assessment of model accuracy, discrimination ability, and overall balance in performance, particularly in the context of imbalanced datasets.</p><p><strong>Results: </strong>our study uncovered key factors influencing diabetes risk and evaluated the performance of various machine learning models. Feature importance analysis revealed that the most influential predictors of diabetes differ between males and females. For females, the most important factors are triglyceride (TG), basal metabolic rate (BMR), and total cholesterol (CHOL), whereas for males, the key predictors are body Mass Index (BMI), serum glutamate Oxaloacetate Transaminase (SGOT), and Gamma-Glutamyl (GGT). Across the entire dataset, BMI remains the most important variable, followed by SGOT, BMR, and energy intake. These insights suggest that gender-specific risk profiles should be considered in diabetes prevention and management strategies. In terms of model performance, our results show that ADASYN with MLP classifier achieved an F1 score of 82.17 ± 3.38, AUC of 89.61 ± 2.09, and G-means of 89.15 ± 2.31. SMOTE with MLP followed closely with an F1 score of 79.85 ± 3.91, AUC of 89.7 ± 2.54, and G-means of 89.31 ± 2.78. The SMOTEENN with Random Forest combination achieved an F1 score of 78.27 ± 1.54, AUC of 87.18 ± 1.12, and G-means of 86.47 ± 1.28.</p><p><strong>Conclusion: </strong>These combinations effectively address class imbalance, improving the accuracy and reliability of diabetes predictions. The findings highlight the importance of using appropriate data-balancing techniques in medical data analysis.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"24 1","pages":"220"},"PeriodicalIF":3.9,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11430121/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142341476","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 Bayesian multivariate hierarchical model for developing a treatment benefit index using mixed types of outcomes.","authors":"Danni Wu, Keith S Goldfeld, Eva Petkova, Hyung G Park","doi":"10.1186/s12874-024-02333-z","DOIUrl":"10.1186/s12874-024-02333-z","url":null,"abstract":"<p><strong>Background: </strong>Precision medicine has led to the development of targeted treatment strategies tailored to individual patients based on their characteristics and disease manifestations. Although precision medicine often focuses on a single health outcome for individualized treatment decision rules (ITRs), relying only on a single outcome rather than all available outcomes information leads to suboptimal data usage when developing optimal ITRs.</p><p><strong>Methods: </strong>To address this limitation, we propose a Bayesian multivariate hierarchical model that leverages the wealth of correlated health outcomes collected in clinical trials. The approach jointly models mixed types of correlated outcomes, facilitating the \"borrowing of information\" across the multivariate outcomes, and results in a more accurate estimation of heterogeneous treatment effects compared to using single regression models for each outcome. We develop a treatment benefit index, which quantifies the relative benefit of the experimental treatment over the control treatment, based on the proposed multivariate outcome model.</p><p><strong>Results: </strong>We demonstrate the strengths of the proposed approach through extensive simulations and an application to an international Coronavirus Disease 2019 (COVID-19) treatment trial. Simulation results indicate that the proposed method reduces the occurrence of erroneous treatment decisions compared to a single regression model for a single health outcome. Additionally, the sensitivity analyses demonstrate the robustness of the model across various study scenarios. Application of the method to the COVID-19 trial exhibits improvements in estimating the individual-level treatment efficacy (indicated by narrower credible intervals for odds ratios) and optimal ITRs.</p><p><strong>Conclusion: </strong>The study jointly models mixed types of outcomes in the context of developing ITRs. By considering multiple health outcomes, the proposed approach can advance the development of more effective and reliable personalized treatment.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"24 1","pages":"218"},"PeriodicalIF":3.9,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11437666/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142341570","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":"Sample size recalculation in three-stage clinical trials and its evaluation.","authors":"Björn Bokelmann, Geraldine Rauch, Jan Meis, Meinhard Kieser, Carolin Herrmann","doi":"10.1186/s12874-024-02337-9","DOIUrl":"https://doi.org/10.1186/s12874-024-02337-9","url":null,"abstract":"<p><strong>Background: </strong>In clinical trials, the determination of an adequate sample size is a challenging task, mainly due to the uncertainty about the value of the effect size and nuisance parameters. One method to deal with this uncertainty is a sample size recalculation. Thereby, an interim analysis is performed based on which the sample size for the remaining trial is adapted. With few exceptions, previous literature has only examined the potential of recalculation in two-stage trials.</p><p><strong>Methods: </strong>In our research, we address sample size recalculation in three-stage trials, i.e. trials with two pre-planned interim analyses. We show how recalculation rules from two-stage trials can be modified to be applicable to three-stage trials. We also illustrate how a performance measure, recently suggested for two-stage trial recalculation (the conditional performance score) can be applied to evaluate recalculation rules in three-stage trials, and we describe performance evaluation in those trials from the global point of view. To assess the potential of recalculation in three-stage trials, we compare, in a simulation study, two-stage group sequential designs with three-stage group sequential designs as well as multiple three-stage designs with recalculation.</p><p><strong>Results: </strong>While we observe a notable favorable effect in terms of power and expected sample size by using three-stage designs compared to two-stage designs, the benefits of recalculation rules appear less clear and are dependent on the performance measures applied.</p><p><strong>Conclusions: </strong>Sample size recalculation is also applicable in three-stage designs. However, the extent to which recalculation brings benefits depends on which trial characteristics are most important to the applicants.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"24 1","pages":"214"},"PeriodicalIF":3.9,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11423520/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142341478","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":"Correction: A non-parametric approach to predict the recruitment for randomized clinical trials: an example in elderly inpatient settings.","authors":"Alejandro Villasante-Tezanos, Yong-Fang Kuo, Christopher Kurinec, Yisheng Li, Xiaoying Yu","doi":"10.1186/s12874-024-02343-x","DOIUrl":"https://doi.org/10.1186/s12874-024-02343-x","url":null,"abstract":"","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"24 1","pages":"213"},"PeriodicalIF":3.9,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11414120/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142280252","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":"Challenges and opportunities of translating animal research into human trials in Ethiopia","authors":"Askale Abrhaley, Mirutse Giday, Asrat Hailu","doi":"10.1186/s12874-024-02338-8","DOIUrl":"https://doi.org/10.1186/s12874-024-02338-8","url":null,"abstract":"Although the goal of translational research is to bring biomedical knowledge from the laboratory to clinical trial and therapeutic products for improving health, this goal has not been well achieved as often as desired because of many barriers documented in different countries. Therefore, the aim of this study was to investigate the challenges and opportunities of translating animal research into human trials in Ethiopia. A descriptive qualitative study, using in-depth interviews, was conducted in which preclinical and clinical trial researchers who have been involved in animal research or clinical trials as principal investigator were involved. Data were analyzed using inductive thematic process. Six themes were emerged for challenges: lack of financial and human capacity, inadequate infrastructure, operational obstacles and poor research governance, lack of collaboration, lack of reproducibility of results and prolonged ethical and regulatory approval processes. Furthermore, three themes were synthesized for opportunities: growing infrastructure and resources, improved human capacity and better administrative processes and initiatives for collaboration. The study found that the identified characteristics/features are of high importance either to hurdle or enable the practice of translating animal research into human trials. The study suggests that there should be adequate infrastructure and finance, human capacity building, good research governance, improved ethical and regulatory approval process, multidisciplinary collaboration, and incentives and recognition for researchers to overcome the identified challenges and allow translating of animal research into human trials to proceed more efficiently.","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"28 1","pages":""},"PeriodicalIF":4.0,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142248459","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A theoretical framework for linking hospitals longitudinally: demonstrated using German Hospital Quality Reports 2016–2020","authors":"Limei Ji, Max Geraedts, Werner de Cruppé","doi":"10.1186/s12874-024-02317-z","DOIUrl":"https://doi.org/10.1186/s12874-024-02317-z","url":null,"abstract":"In longitudinal health services research, hospital identification using an ID code, often supplemented with several additional variables, lacks clarity regarding representativeness and variable influence. This study presents an operational method for hospital identity delimitation and a novel longitudinal identification approach, demonstrated using a case study. The conceptualisation considers hospitals as evolving entities, identifying “similar enough” pairs across two time points using an automated similarity matrix. This method comprises key variable selection, similarity scoring, and tolerance threshold definition, tailored to data source characteristics and clinical relevance. This linking method is tested by applying the identification of minimum caseload requirements-related German hospitals, utilizing German Hospital Quality Reports (GHQR) 2016–2020. The method achieved a success rate (min: 97.9% - max: 100%, mean: 99.9%) surpassing traditional hospital ID-code linkage (min: 91.5% - max: 98.8%, mean: 96.6%), with a remarkable 99% reduction in manual work through automation. This method, rooted in a comprehensive understanding of hospital identities, offers an operational, automated, and customisable process serving diverse clinical topics. This approach has the advantage of simultaneously considering multiple variables and systematically observing temporal changes in hospitals. It also enhances the precision and efficiency of longitudinal hospital identification in health services research.","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"126 1","pages":""},"PeriodicalIF":4.0,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142248457","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An exploration of available methods and tools to improve the efficiency of systematic review production: a scoping review","authors":"Lisa Affengruber, Miriam M. van der Maten, Isa Spiero, Barbara Nussbaumer-Streit, Mersiha Mahmić-Kaknjo, Moriah E. Ellen, Käthe Goossen, Lucia Kantorova, Lotty Hooft, Nicoletta Riva, Georgios Poulentzas, Panagiotis Nikolaos Lalagkas, Anabela G. Silva, Michele Sassano, Raluca Sfetcu, María E. Marqués, Tereza Friessova, Eduard Baladia, Angelo Maria Pezzullo, Patricia Martinez, Gerald Gartlehner, René Spijker","doi":"10.1186/s12874-024-02320-4","DOIUrl":"https://doi.org/10.1186/s12874-024-02320-4","url":null,"abstract":"Systematic reviews (SRs) are time-consuming and labor-intensive to perform. With the growing number of scientific publications, the SR development process becomes even more laborious. This is problematic because timely SR evidence is essential for decision-making in evidence-based healthcare and policymaking. Numerous methods and tools that accelerate SR development have recently emerged. To date, no scoping review has been conducted to provide a comprehensive summary of methods and ready-to-use tools to improve efficiency in SR production. To present an overview of primary studies that evaluated the use of ready-to-use applications of tools or review methods to improve efficiency in the review process. We conducted a scoping review. An information specialist performed a systematic literature search in four databases, supplemented with citation-based and grey literature searching. We included studies reporting the performance of methods and ready-to-use tools for improving efficiency when producing or updating a SR in the health field. We performed dual, independent title and abstract screening, full-text selection, and data extraction. The results were analyzed descriptively and presented narratively. We included 103 studies: 51 studies reported on methods, 54 studies on tools, and 2 studies reported on both methods and tools to make SR production more efficient. A total of 72 studies evaluated the validity (n = 69) or usability (n = 3) of one method (n = 33) or tool (n = 39), and 31 studies performed comparative analyses of different methods (n = 15) or tools (n = 16). 20 studies conducted prospective evaluations in real-time workflows. Most studies evaluated methods or tools that aimed at screening titles and abstracts (n = 42) and literature searching (n = 24), while for other steps of the SR process, only a few studies were found. Regarding the outcomes included, most studies reported on validity outcomes (n = 84), while outcomes such as impact on results (n = 23), time-saving (n = 24), usability (n = 13), and cost-saving (n = 3) were less often evaluated. For title and abstract screening and literature searching, various evaluated methods and tools are available that aim at improving the efficiency of SR production. However, only few studies have addressed the influence of these methods and tools in real-world workflows. Few studies exist that evaluate methods or tools supporting the remaining tasks. Additionally, while validity outcomes are frequently reported, there is a lack of evaluation regarding other outcomes.","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"16 1","pages":""},"PeriodicalIF":4.0,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142248517","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"“When selling anything to an audience, visible publicity is key:” experiences, barriers, and enablers to participate in a COVID-19 study in Malawi","authors":"Vanessa Mdala, Deborah Nyirenda, Samuel Mpinganjira, Victor Mwapasa, Alinane Linda Nyondo-Mipando","doi":"10.1186/s12874-024-02329-9","DOIUrl":"https://doi.org/10.1186/s12874-024-02329-9","url":null,"abstract":"Many studies in infectious diseases struggle to recruit participants. The SARS-CoV-2 infection, transmission dynamics, and household impact in Malawi (SCATHIM) study reported a refusal rate of 57.2%. Adequate publicity can lead to more people participating in studies. This study explored the reasons for participating in the SCATHIM study. A descriptive qualitative study informed by the theory of reasoned action was conducted in Blantyre between January 2022 and March 2022 to assess factors that influence participation in a COVID-19 study among 10 index cases, 10 caregivers, 10 study decliners, and 5 research staff. The data were collected via in-depth interview guides, audio recorded, transcribed, managed via NVIVO and analysed via a thematic approach. The factors that motivated participation in the study included one’s knowledge of COVID-19; potential access to medical services, including free COVID-19 tests for members of the household; financial reimbursements; and the ability to contribute scientific knowledge. The barriers to participation included minimal publicity of the study amidst a novel condition, perceived stigma and discrimination, perceived invasion of privacy, discomfort with the testing procedures, and suboptimal financial reimbursements. Effective publicity and outreach strategies have the potential to decrease refusal rates in study participation, especially if a condition is novel. Studies on infectious diseases should address stigma and discrimination to promote participation and ensure participant safety.","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"42 1","pages":""},"PeriodicalIF":4.0,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142248519","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Conducting two evidence syntheses in six weeks – experiences with and evaluation of a pilot project","authors":"Heather Melanie R. Ames, Hege Kornør, Line Holtet Evensen, Ingeborg Beate Lidal, Elisabet Hafstad, Christine Hillestad Hestevik, Patricia Sofia Jacobsen Jardim, Gyri Hval","doi":"10.1186/s12874-024-02334-y","DOIUrl":"https://doi.org/10.1186/s12874-024-02334-y","url":null,"abstract":"Evidence synthesis organisations are trying to meet commissioners’ needs for rapid responses to their evidence synthesis commissions. In this project we piloted an intensive process, working to complete evidence syntheses within six-weeks, rather than the standard lead time of 4–6 months. Our objective was to explore how researchers experience working intensively, identify barriers and facilitators, and determine how a more intensive approach to evidence synthesis could be more systematically introduced in the future. In a pre-planning phase, an intensive work group was established, and two commissions were selected for this pilot project. The evidence synthesis process was divided into two phases: planning and intensive. The planning phase, involved scheduling the intensive phase, exploring new digital tools, and identifying peer reviewers. The intensive phase encompassed the entire evidence synthesis process. Two review teams were formed, each with a team lead supported by a process lead and leadership contact point. Throughout the project, teams engaged in reflective meetings to evaluate and adjust processes as needed. During the planning phase, teams identified significant uncertainties regarding scopes, research questions, and inclusion criteria. To address this, they engaged with commissioners earlier than originally planned, clarified these aspects, and prepared protocols. Despite some minor deviations from the original plan, both reviews were completed on schedule, with one team expanding their scope due to the absence of eligible studies. Teams operated flexibly, held regular meetings, and found the process seamless due to fewer interruptions. Machine learning tools facilitated rapid study selection. The process lead role, created to guide and evaluate the project, proved beneficial, providing structure and support, although clearer role delineation with the leadership contact point could have improved efficiency. Overall, the intensive process fostered focus and productivity, allowing teams to manage short-term deliverables effectively. The researchers preferred working intensively with one evidence synthesis over being involved with many projects at the same time. They felt that time use was more effective, and they were able to complete the tasks in a focused way. However, there are several implications that should be considered before implementing an intensive approach in future evidence syntheses.","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"3 1","pages":""},"PeriodicalIF":4.0,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142248520","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}