Differences in the epidemiology of out-of-hospital and in-hospital trauma deaths
Bibliographic record
Abstract
BACKGROUND: Trauma is a leading cause of mortality. Holistic views of trauma systems consider injury as a public health problem that requires efforts in primary, secondary and tertiary prevention. However, the performance of trauma systems is commonly judged on the in-hospital mortality rate. Such a focus misses opportunities to consider all deaths within a population, to understand differences in in-hospital and out-of-hospital trauma deaths and to inform population-level injury prevention efforts. The aim of this study was to provide an epidemiological overview of out-of-hospital and in-hospital trauma deaths in a geographically-defined area over a 10-year period. METHODS: We performed a population-based review of out-of-hospital and in-hospital trauma deaths over the period of 01 July 2006 to 30 June 2016 in Victoria, Australia, using data from the National Coronial Information System and the Victorian State Trauma Registry. Temporal trends in population-based incidence rates were evaluated. RESULTS: Over the study period, there were 11,246 trauma deaths, of which 71% were out-of-hospital deaths. Out-of-hospital trauma deaths commonly resulted from intentional self-harm events (50%) and transport events (35%), while in-hospital trauma deaths commonly resulted from low falls (≤1 metre) (50%). The incidence of overall trauma deaths did not change over the study period (incidence rate ratio 0.998; 95%CI: 0.991, 1.004; P = 0.56). CONCLUSIONS: Out-of-hospital deaths accounted for most trauma deaths. Given the notable differences between out-of-hospital and in-hospital trauma deaths, monitoring of all trauma deaths is necessary to inform injury prevention activities and to reduce trauma mortality. The absence of a change in the incidence of both out-of-hospital and in-hospital trauma deaths demonstrates the need for enhanced activities across all aspects of injury prevention.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".