Lateral damage and point of impactin intersection crashes: Implications for injury
Bibliographic record
Abstract
Crashes in intersections may result in damage to vehicles and injury to occupants in many different ways. One vehicle hitting the side of another (T-type) is the classic side impact; however, we have argued in the past that other crash configurations (e.g., L-type) may subject occupants to similar risks because both vehicles may sustain lateral damage. To test this assumption, we examined crash data from police reports of 4032 intersection right-angle crashes (IRC), collected by the Insurance Corporation of British Columbia for 2002. We compared the risk and types of injury in target and bullet vehicles for T-type crashes, L-type crashes by front and rear fender involvement and for all other IRC crashes. There were 787 T-type crashes (impact into either side of target vehicle), compared to 798 L-type crashes (impact into front fender) and 350 L-type (impact into rear fender). Overall, injury risk was 23.5%. Proportions injured were very similar for occupants of target and bullet vehicles in T-type crashes (OR = 0.996; 95% ci 0.80 to 1.24.); for L-type crashes, the proportions were 23.2% for front and/or front fender involvement and 15.0% for crashes involving the rear fender of one vehicle and the front or front fender of the other (OR =1.71; 95% ci 1.40 - 2.10). Apart from rear fender crashes, proportions injured were very similar (P > 0.05). Other factors, notably weather, lighting, land use and vehicle damage differed significantly by crash type, and were strongly associated with injury risk. Since rear-fender crashes are a small proportion of IRC crashes, this suggests that it is not necessary to subdivide crashes by configuration in IRC crashes.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".