THE RISK OF INJURY AND VEHICLE DAMAGE SEVERITY IN VEHICLE MISMATCHED SIDE IMPACT CRASHES IN BRITISH COLUMBIA
Bibliographic record
Abstract
As occupant protection offered by new passenger vehicles has improved, there has been growing concern about the harm that some vehicle designs may inflict on occupants of other vehicles with which they collide. Preceding analyses of crash statistics have clearly demonstrated the incompatibility between passenger sedan cars (PS) and pick-up trucks (PU) involved in side impact crashes in British Columbia. A comparison of light truck and passenger car crashes in previous literature reveals that light truck vehicles inflict greater harm than passenger cars for a number of reasons including their greater weight, stiffer structure, and higher ride height. These features place occupants of passenger cars at a disadvantage should they be involved in a collision with a light truck vehicle. The injury risk for passenger sedan car occupants is greater than the risk for pick-up truck occupants in two-vehicle crashes (Odds Ratio (OR) 1.87; 95% Confidence Interval (CI) 1.38-2.52). In addition, the risk of vehicle damage severity was increased for passenger cars compared with pick-up trucks (write off vehicle-OR 5.35; 95% CI 3.75-7.63, severely damaged vehicles-OR 5.87; 95% CI 4.79–7.19, moderately damaged vehicles-OR 2.86; 95% CI 2.44–3.36). There is strong justification for injury prevention experts and policy makers to step up motor vehicle crash injury prevention advocacy by implementing evidence-based policies to reduce rates of injury as a result of passenger sedan cars and pick-up trucks involved in side impact crashes in the province of British Columbia.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".