Establishing an Essential Dataset for Trauma Registry in LMICs: Insights From a Delphi Survey
Bibliographic record
Abstract
BACKGROUND: Injury is a leading cause of morbidity and mortality globally, with 90% of deaths occurring in low-middle-income countries (LMICs). Establishing well-functioning trauma systems is crucial in LMICs, with a trauma registry being an integral component. This study used the Delphi Technique to gather insights from trauma experts on essential data variables for adult trauma registries in LMICs. It aimed to identify critical variables that can improve trauma care in resource-constrained settings. METHODS: A two-round Delphi survey was conducted from October 2023 to June 2024, engaging trauma specialists from diverse regions. Experts evaluated variables as essential, optional, or excluded, with a consensus of 70% agreement. Feedback from the first round informed the second round, focusing on variables lacking consensus. RESULTS: In the first round, 37 variables reached consensus as essential, including demographics, injury-related data, prehospital information, some clinical assessment variables, injury classification, road traffic accident data, and patient outcome data. The second round identified additional variables and categorized others as optional, including education level, income level, certain advanced imaging modalities, cost of care, and some outcome measures. Birthplace was identified as the only variable for exclusion from the trauma registry. CONCLUSIONS: This study identifies essential elements of a trauma registry in LMICs, leveraging insights from experts experienced in resource-limited settings. These recommendations ensure relevance and feasibility for implementation. Establishing such a registry is crucial for quality assurance, jurisdictional comparisons, and the foundation of trauma systems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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; a candidate call from one teacher head, not a consensus.
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".