Severe injury mechanisms in two paediatric trauma centres: Determination of prevention priorities
Bibliographic record
Abstract
BACKGROUND: To meet community needs, injury prevention programs for children should be targeted to trends in objective data on mechanisms of injury. The aim of the present study was to identify the most important severe injury mechanisms. METHODS: The present study retrospectively reviewed severe paediatric trauma patients in two regional trauma centres. Injury prevention priority scores were computed using different severity measures - injury severity score (ISS), revised trauma score, trauma-related injury severity score, Glasgow Coma Scale (GCS) and mortality - to identify prevention priorities. RESULTS: A total of 3732 children with severe injury were identified; mean age (+/-SD) was 9.0+/-5.2 years and 2469 (66.2%) were boys. The GCS was 7 or lower in 209 patients (5.6%) and the median ISS was 9. Overall, there were 77 deaths (2.1%). 'Fall from height' was the most frequent mechanism of injury, and 'motor vehicle traffic injury' resulted in the most severe injury. The most significant mechanisms of injury, using ISS, were 'fall from height', 'motor vehicle traffic injury', 'pedestrian struck by motor vehicle', 'bicycle injuries' and 'child abuse'. Different priorities were identified depending on the severity measures used - 'fall from height' would be the priority with ISS, revised trauma score and trauma-related injury severity score; 'motor vehicle traffic injury' with mortality and 'drowning/submersion' with GCS. 'Fall from height' was the highest ranked mechanism of injury in one centre compared with 'motor vehicle traffic injury' in the other. Younger children tended to have injuries as a result of falls, while adolescents had more motor vehicle occupant injuries. Failure to use safety devices, such as helmets and seat belts, was a common finding among severely injured children. CONCLUSION: The present study shows that the severe injury prevention priorities identified vary depending on the severity measures used. The variations seen across age groups and between the two centres are also important factors that must be taken into account when developing prevention programs or considering research initiatives.
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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 | 0.002 | 0.000 |
| 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".