Minimal Data Elements for Surveillance and Reporting Of Musculoskeletal injuries in the MILitary (ROMMIL) International Consensus Statement
Bibliographic record
Abstract
INTRODUCTION: A systematic approach to collecting and reporting injury data in military settings is necessary to maximize the impact of musculoskeletal injury-related research. An international consensus on recommended core data set of elements to capture and report is necessary. The purpose was to summarize the process and results from an international consensus study to establish recommended common minimum data elements for surveillance and Reporting Of Musculoskeletal injuries in the MILitary (ROMMIL). METHODS: A 10-step hybrid consensus process was performed. Knowledge users were embedded in the process for co-creation of pertinent questions, data elements, and voting. Evidence synthesis included a scoping review on the barriers and facilitators to implementing injury prevention programs, followed by a knowledge user survey. A sequential three round Delphi study refined and validated the final elements in the recommendation checklist. Consensus recommendations were presented to an international audience of stakeholders. Participants voted on each statement with 0 representing no importance, 5 somewhat important, and 10 maximum importance. RESULTS: The consensus recommendation includes one data principle of keeping continuous data continuous and 33 minimum data elements. Data elements include demographics, lifestyle, service branch, musculoskeletal/surgical history, exposure, and injury characteristics. The data principle endorsed by knowledge users recommends that continuous variables (e.g., age, weight, exposure) remain continuous and not categorized into groups. Dissenting viewpoints are detailed to provide fair and balanced consensus recommendations. CONCLUSIONS: The ROMMIL checklist could be leveraged by clinicians, researchers, and knowledge users working in military settings when comparing and harmonizing data across studies, service branches, and countries. The ROMMIL checklist will support improved data synthesis to better inform evidence-based practice in military medicine, and the ability to generate more useful prognostic models to quantify injury risk.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.306 | 0.392 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.008 |
| Bibliometrics | 0.014 | 0.008 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.012 | 0.014 |
| Research integrity | 0.011 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".