The Risk of Injury and Vehicle Damage in Intersection Right-Angle Crashes
Bibliographic record
Abstract
We compared 4032 ‘intersection, right-angle’ crashes (IRC), and a random sample of other two-vehicle crashes, selected after stratifying on driver age from all police-reported crashes in British Columbia, Canada in 2002. The proportion of injured occupants varied from 20.8% (control crashes, older drivers) to 27.8% (IRC, younger drivers). Whiplash was the most frequently reported injury (8–10% of all vehicle occupants) but was less common in IRC crashes than other two-vehicle crashes. Overall the odds of injury was 30% higher in IRC crashes than other crashes after controlling for environmental factors. Damage to the vehicles was also markedly higher for IRC crashes. When extent of damage was controlled the odds of injury to occupants was only 13% higher. For specific injuries, however, notably concussion (OR = 1.89) and fracture (OR = 1.54), a significant increase in risk remained. Whiplash, in contrast, was significantly less frequent (OR = 0.85). IRC crashes typically involve lateral damage to one or both vehicles; these crashes are associated not only with a higher risk of vehicle damage but also with a higher risk of many types of injury beyond what may be due to vehicle damage. In short, intersection crashes are bad news; they require more effective strategies, both for vehicle design and for traffic control to reduce crashes and protect people when these crashes occur.
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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 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".