Risk factors predisposing to pedestrian road traffic injury in children living in Lima, Peru: a case–control study
Bibliographic record
Abstract
OBJECTIVE: To describe the epidemiology of pedestrian road traffic injury in Lima and to identify associated child-level, family-level, and school travel-related variables. DESIGN: Case-control study. SETTING: The Instituto Nacional de Salud del Niño, the largest paediatric hospital in the city. PARTICIPANTS: Cases were children who presented because of pedestrian road traffic injury. Controls presented with other diagnoses and were matched on age, sex and severity of injury. RESULTS: Low socioeconomic status, low paternal education, traffic exposure during the trip to school, lack of supervision during outside play, and duration of outside play were all statistically significantly associated with case-control status. In multivariate logistic regression, a model combining the lack of supervision during outside play and the number of the streets crossed walking to school best predicted case-control status (p<0.001). CONCLUSIONS: These results emphasise that an assessment of children's play behaviours and school locations should be considered and integrated into any plan for an intervention designed to reduce pedestrian road traffic injury. A child-centred approach will ensure that children derive maximum benefit from sorely needed public health interventions.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".