Driver and Front Seat Passenger Fatalities Associated with Air Bag Deployment. Part 1: A Canadian Study
Bibliographic record
Abstract
Real world motor vehicle collision research of injuries due to deployment of "first-generation" air bags has been conducted by Transport Canada since 1993. Fifty-three fatal crashes (36 frontal impacts; 17 side collisions) involving 48 drivers and 10 right front passengers were reviewed. In the Canadian data, air bag deployment in five of nine low severity frontal crashes (delta-V (deltaV) < 25 km/h or 15 mph) was linked to five deaths, four of whom were autopsied (four adults with craniocervical (basal skull and C2 fracture with brainstem avulsion; "closed head injury"--no autopsy) or chest trauma (aortic or pulmonary artery tears); one child with atlanto-occipital dislocation). An occupant who is close ("out-of-position") to the air bag at the time of deployment is at risk for injury. In 27 high severity frontal impacts, unusual (e.g., pulmonary "blast" hemorrhage in one autopsied case) or isolated potentially survivable injuries (e.g., clinically documented ruptured right atrium; probable flail chest observed during the autopsy on a decomposed body) localized to the head, neck or chest in three possibly out-of-position drivers pointed to the deployed air bag as a source of injury. In one of 17 side collisions an out-of-position driver sustained a radiographically confirmed C1-C2 dislocation in a minimally intruded vehicle.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".