Collision investigations of child pedestrians or cyclists stuck by motor vehicles:
Bibliographic record
Abstract
Background Collisions involving child pedestrians and cyclists represent a large injury burden in Canadians less than 14 years of age. Although motor vehicle collision studies investigating occupants and their injuries are well-established, collisions involving child pedestrians/cyclists have not been reconstructed in this manner. The purpose of this study was to develop a methodology for investigation of child cyclist and pedestrian collisions in order to reconstruct crash events based on data at the scene and medical assessment in hospital, and to discuss potential countermeasures. Methods Children aged 4–15 were included in the study if they were a pedestrian or cyclist involved in a collision with a motor vehicle and as a result, were admitted to the Hospital for Sick Children (HSC) in Toronto, Canada or stayed in the emergency department for over 12 h between 3 July 2007 and 31 October 2008. Crash scene investigations were performed by a collision investigator in collaboration with the Toronto Police Traffic Services Division. Medical trauma data collection was done via the child's medical charts at the HSC. Results Three child pedestrian and one child cyclist versus motor vehicle collisions were successfully reconstructed. Mechanisms of injury were determined. Discussion Investigations of child pedestrian and cyclist versus motor vehicle collisions provide useful insights regarding injury profiles and countermeasures. There were numerous inadequacies in the built-environment which set the stage for these collisions. Focus of countermeasures for prevention of child pedestrian and cyclist injuries should be directed at the built environment.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".