SEVERE INJURY MECHANISMS: DETERMINATION OF PREVENTION PRIORITIES
Bibliographic record
Abstract
Objective Injury prevention programmes for children should choose their target from objective data on mechanisms of injury. This study was done to identify the most important severe injury mechanisms. Methods Retrospective review of severe paediatric trauma patients in two regional trauma centres. Injury prevention priority scores were computed with different severity measures to identify prevention priorities: injury severity score (ISS), revised trauma score (RTS), trauma-related injury severity score (TRISS), Glasgow coma scale (GCS) and mortality. Results A total of 3732 children with severe injury were identified with a mean age of 9.0 ± 5.2 years (±SD); 2469 were boys (66.2%). GCS was 7 or less in 209 patients (5.6%) and median ISS was 9. Overall, there were 77 deaths (2.1%). “Fall from height” was the most frequent mechanism and “motor vehicle traffic injury” resulted in the most severe injuries. The most significant mechanisms, using ISS, were “fall from height”, “motor vehicle traffic injury”, “pedestrian”, “bicycle injuries” and “child abuse”. Different priorities were identified depending on the severity measures used: “fall from height” would be the priority with ISS, RTS and TRISS, “motor vehicle traffic” with mortality and “drowning/submersion” with GCS. Failure to use safety devices, such as helmets and seat belts, was a common finding among severely injured children. Conclusion This study shows that injury prevention priorities identified vary depending on the severity measures used. The variations seen across age groups and centres are factors that must be taken into account when developing prevention programmes.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".