A population based investigation of head injuries and symptoms of concussion of children and adolescents in schools
Bibliographic record
Abstract
OBJECTIVE: To examine the incidence of head injury and symptoms of concussion among children at school and to determine the relationship of age, gender, and cause to incidence rates. DESIGN: Incident reports involving head injury for schools in the Province of Ontario, Canada during the year 2000 were evaluated. PARTICIPANTS: The population base for the schools represented was 1 372 979 children aged 6 to 16. SETTING: 95% of schools in the province of Ontario, Canada participated in the injury reporting system. MAIN OUTCOME MEASURES: A head injury was defined as any injury to the head that came to the attention of a school official. Head injuries accompanied by symptoms of concussion became a secondary outcome measure. RESULTS: There were 11 068 unduplicated head injury reports for the year 2000 of which 1861 qualified as producing signs or symptoms of concussion. Young children were more likely to have a head injury than older children, but slightly less likely to experience concussive symptoms. The primary cause of injury to young children was falls. Older children were more likely to receive head injuries and symptoms of concussion from sports activities. CONCLUSIONS: Overall rate of injury (3.98 per 100 children) was consistent with previous studies using prospective injury reporting systems. Probability of a head injury with symptoms of concussion among schoolchildren was only 1.9% for boys and <1% for girls during the course of their school years. There is ample justification for prevention efforts in schools.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".