Epidemiology of severe paediatric trauma following winter sport accidents
Bibliographic record
Abstract
AIM: This study describes the epidemiology of severe injuries related to winter sports (skiing, snowboarding and sledding) in children and assesses potential preventive actions. METHODS: A single-centre retrospective study performed at Pediatric or Adult Intensive Care Unit in the French Alps. All patients less than 15 years old, admitted to the Intensive Care Unit following a skiing, snowboarding or sledding accident from 2011 to 2018, were included. RESULTS: We included 186 patients (mean age 10.6 years and 68% were male); of which 136 (73%), 21 (11%) and 29 (16%) had skiing, snowboarding and sledding accidents, respectively. The average ISS (injury severity score) was 16. The major lesions were head (n = 94 patients, 51%) and intra-abdominal (n = 56 patients, 30%) injuries. Compared to skiing/snowboarding, sledding accidents affected younger children (7 vs 11 years, P < .001); most of whom did not wear a helmet (89% vs 8%, P < .001). Severity scores were statistically different amongst winter sports (ISS = 16 (IQR 9-24) for skiing, 9 (IQR 4-16) for snowboarding and 16 (IQR 13-20) for sledding accident, P = .02). CONCLUSION: Winter sports can cause severe trauma in children. Sledding accidents affect younger children that may benefit from wearing protective equipment.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".