• Reginald T A Conley, Zodina Beiene, Charlotte Lenz, and Meir T Marmor.
• Orthopaedic Trauma Institute, University of California, San Francisco, 2540 23rd Street, Bldg 7, 3rd Floor, Rm 3110, San Francisco, CA 94110, USA. Electronic address: reginald.conley@ucsf.edu.
• Injury. 2025 Feb 1; 56 (2): 112161112161.

IntroductionThe development of national registries from routinely collected health data has transformed the research landscape by improving access to large sample populations. This growing volume of data enables researchers to address critical questions but also challenges clinicians in conducting, evaluating, and applying the research. The National Trauma Data Bank (NTDB), the largest aggregate of deidentified trauma data in the world, is increasingly utilized for retrospective studies on trauma. This scoping review aimed to assess the quality of reporting of NTDB-based orthopedic trauma publications.MethodsWe queried the Dimensions database for orthopedic studies using the NTDB. The quality of reporting was assessed by adherence to two international publication guidelines: the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement and the REporting of studies Conducted using Observational routinely collected data (RECORD).ResultsFrom a total of 3,720 identified articles, 137 manuscripts were available for analysis. The median scores and interquartile ranges (IQR) for STROBE and RECORD were 19 (IQR 18-20) and 7 (IQR 7-8), respectively. For STROBE scoring, the lowest fulfilled items were handling missing data and potential sources of bias. For RECORD scoring, the lowest fulfilled items were accessibility to protocol, raw code and data, validation studies, and data cleaning. A greater proportion of high-scoring studies were published in high-impact journals versus low-impact journals and in journals that enforced guidelines versus those that did not.ConclusionThis study highlights the methodological gaps in the NTDB-based orthopedic trauma publications and identifies areas for improvement, including the management of missing data, selection of the study population through data cleaning, identification of sources of bias, and transparency in data accessibility. Future work should test the reproducibility of these studies and evaluate adherence to established guidelines across a broader range of databases and disciplines.Copyright © 2025 Elsevier Ltd. All rights reserved.

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