Prevention of severe injuries in preschool children: what are the priorities?
Bibliographic record
Abstract
Injury is the leading cause of death in childhood. Most of these injuries are preventable events. With limited resources, strategies for prevention should target severe injuries and be based on developmental stage. The aim of this study was to characterise mechanisms of severe injuries in children aged 1 to 4 years old. Methods Retrospective review of trauma registry of two paediatric trauma centres in Quebec between January 1999 and April 2006. The Injury Prevention Priority Score (IPPS) and Injury Severity Score (ISS) assigned a priority rank to mechanisms of severe injuries based on frequency and on severity. Results A total of 640 children 1 to 4 years old with severe injury were identified. Glasgow coma score (GCS) was 7 or less in 33 patients (5%) and mean length of stay was 6.4 days. There were 15 deaths (2.3%). IPPS identified respectively Fall from height, Motor vehicle injuries, Child abuses, Pedestrians struck by motor vehicle and Cycle injuries as the most important mechanisms. The majority of falls occurred at home, mostly in stairs. Less than 30% of victims of motor vehicle accident were adequately restrained. There were 11 all-terrain vehicle-related injuries as a driver or a passenger even in children less than 4 years of age. Conclusion This study highlights mechanisms that can be targeted by community prevention programs to fight injuries with the highest mortality and morbidity in 1 to 4 years old children.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".