THE INJURY SEVERITY RATE DIFFERENCES IN PASSENGER CARS AND PICK UP TRUCKS RELATED TWO VEHICLE INVOLVED MOTOR VEHICLE CRASHES IN BRITISH COLUMBIA, CANADA
Bibliographic record
Abstract
The effect of large vehicle involvement on motor vehicle crash (MVC) rates and severity has long been a concern in MVC analysis literature. Injuries in drivers and occupants are related to several key factors: the mass of the case vehicle and mass of its collision partner and speed of case vehicle and collision partner at the time of the crash. Objective: To evaluate the relative risk of injury occurrence in collisions between picks up trucks (PU) and passenger sedan cars (PS). Methods: Data from the Insurance Corporation of British Columbia (ICBC) crash data base was used to determine MVC rates and injury occurrence. Descriptive characteristics of the injury location and injury type were analyzed comparing the Odds Ratios and chi-squares. Results: PS occupants received more injuries; Odds Ratio was 2.49 (95% confidence interval: 2.15–2.88). Conclusion: Occupants in PS which collide with PU were at twice the risk of injuries. Concussion, whiplash, lacerations and abrasion were more frequent in PS drivers and occupants than in PU drivers and occupants. Overall, PS drivers/occupants experienced greater injuries than PU drivers/occupants in PU-PS collisions. In this paper, results are shown as odds ratios comparing occupants injuries in PS (case group) with occupant injuries in PU (control group).
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".