Effect of Airbag Deployment on Head Injuries in Severe Passenger Motor Vehicle Crashes in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: The purpose of this study was to identify and analyze factors contributing to both airbag deployment and resulting head injuries (HIs) and to quantify the effect of airbag deployment on head injuries, in terms of odds of head injury and severity, in severe motor vehicle collisions (MVCs). METHODS: Data were derived from severely injured (Injury Severity Score [ISS] > 12) drivers treated at Ontario's lead trauma hospitals (n = 1,272), and included all MVC driver deaths in the province (n = 665) from 1997-98. We conducted an epidemiologic description and a case-control study to compare drivers with and without HIs. Statistical analysis included Pearson's chi2, Wilcoxon rank-sum, and multiple logistic regression tests. RESULTS: Seventy-one percent of drivers were men, peaking in the 25- to 34-year age group. The most common impact involved multiple vehicles (62%) approaching each other. Overall, 59% of crashes had a frontal location of impact. HIs were significantly associated with a lower age (median, 36 vs. 43 years), seat belt use (53% vs. 59%), and airbag deployment (7% vs. 10%), with higher ISS (median, 34 vs. 22), ejection (20% vs. 10%), and mortality rate (44% vs. 35%). Airbag deployment was associated with higher age and seat belt use, and lower ISS, ejection, and deaths. Importantly, there were fewer HIs with the deployment of an airbag (64% vs. 73%) and a lower severity of HI. When logistic regression was used to control for the effects of possible confounders, airbag deployment was not statistically associated with one's odds of HI (odds ratio, 0.827; 95% confidence interval, 0.560-1.220), but ISS, age, and ejection were. CONCLUSION: Airbag deployment did not significantly lower a driver's odds of head injury in a severe MVC, but it did significantly lower the severity of head injury. This is a significant finding, given that 72% of our study population sustained a head injury and the importance of lowering the severity of these head injuries in terms of patients' ultimate outcome. The most important factor associated with head injuries was ejection, which nearly doubled a driver's odds of head injury (odds ratio, 1.759; 95% confidence interval, 1.201-2.577). This reinforces the supplementary protective effect of an airbag and that "buckling up" and keeping occupants in the vehicle is of primary importance in the prevention of head injuries.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".