Back-over Collisions in Child Pedestrians from the Canadian Hospitals Injury Reporting and Prevention Program
Bibliographic record
Abstract
OBJECTIVE: The objective of the current study was to describe the burden of back-over collisions within the context of other child pedestrian collisions as identified through a pediatric emergency room injury surveillance database. METHODS: Injury data for child pedestrian motor vehicle collisions from 1994 to 2003 were obtained from the Canadian Hospitals Injury Reporting and Prevention Program (CHIRPP). Back-over collisions involving children under the age of 14 were identified and classified by written narratives. Characteristics of children involved in back-over collisions were described, and for those admitted to hospital, the nature of injury was compared with other types of child pedestrian collisions. RESULTS: There were 4295 child pedestrian motor vehicle collisions reported to CHIRPP during the study time period. Of the 4295 children, 148 (3.4%) were injured in a back-over collision, with 49 (33.1%) of these collisions involving a vehicle backing out of a driveway. Children involved in back-over collisions were significantly younger than those in forward-moving/other collisions; however, almost 50 percent of back-over collisions involved children older than age 4. Children involved in back-over collisions on driveways were significantly younger than those involved in collisions occurring at other locations. Of those admitted to hospital, children in back-over collisions were more likely to sustain injuries to internal organs. Children in back-over collisions were less likely to sustain severe/mild head injuries and hip/leg fractures. CONCLUSIONS: Although back-over collisions represent a small proportion of pedestrian motor vehicle collisions, they tend to involve more severe injuries, as indicated by their admission to hospital. It was found that older children are also at risk of back-over collisions and back-over collisions occur in areas other than driveways. In order to lessen the burden of back-over collisions, interventions must address children of different ages and a variety of locations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".