A 4-Year Review of Severe Pediatric Trauma in Eastern Ontario: A Descriptive Analysis
Bibliographic record
Abstract
BACKGROUND: The objective of this study was to describe a population of children admitted to a tertiary care pediatric hospital with severe trauma to identify key areas for injury prevention research, and programming. METHODS: Retrospective chart review conducted on all children 0-17 years admitted to the Children's Hospital of Eastern Ontario (CHEO) between April 1, 1996, and March 31, 2000, following acute trauma. Each record was reviewed and assigned an ISS using the AIS 1990 revision. All cases with an ISS > 11 were included in the study. RESULTS: There were 2610 trauma cases admitted to CHEO over the study period. Of these, 237 (9.1%) had severe trauma (ISS > 11). Sixty-two percent were male. Twenty-nine percent were between the ages of 10 and 14 years, 27% between 5 and 9 years, 16% between 15 and 17 years, 15% between 1 and 4 years, and 13% less than 1 year old. The most common mechanisms of injury were due to motor vehicle traffic (39%), falls (24%), child abuse (8%), and sports (5%). Of those resulting from motor vehicle traffic, 53 (57%) were occupants, 22 (24%) were pedestrians, and 18 (19%) were cyclists. When combining traffic and nontraffic mechanisms, 26 (11% of all severe trauma cases) occurred as a result of cycling incidents. The most severe injury in 65% of patients was to the head and neck body region. CONCLUSION: Research efforts and activities to prevent severe pediatric trauma in our region should focus on road safety, protection from head injuries, avoidance of falls, and prevention of child abuse.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".