Injury comparisons between paired drivers and front‐seat passengers in frontal collisions using publicly available crash and injury data
Bibliographic record
Abstract
Forensic engineers and crash safety researchers sometimes use the injuries of a seatbelted occupant to infer the injury risk of an unbelted occupant in the same crash, had they instead been wearing a seatbelt. It is unclear, however, whether this inference is valid or how often two occupants in the same collision have similar injuries. Here, we sought to compare the injury outcomes between drivers and front-seat passengers in frontal collisions using real-world collision data. We compared the injury severity, quantified using the Abbreviated Injury Scale (AIS), of 22 injury categories between front-seat occupants with matching seatbelt use and airbag deployment in single-event frontal collisions recorded in the publicly available National Automotive Sampling System, Crashworthiness Data System (years 1993-2015) database to assess whether they had similar severity injuries. We analyzed the four combinations of seatbelt use and airbag deployment and all seatbelt/airbag conditions combined. In only 3 of 88 combinations of injuries and seatbelt/airbag conditions did more than 50% of occupant pairs have the same AIS score, although the related confidence intervals showed these proportions were not significantly greater than 50%. In contrast, we found 19 combinations of injuries and seatbelt/airbag conditions where one occupant was consistently injured more severely than the other. Our findings show that injury outcome is not similar for both front-seat occupants in the same frontal collision with similar seatbelt and airbag conditions; however, one may be able to predict that one occupant would be more severely injured than their fellow occupant.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".