Opportunities for Injury Reduction in Rollover Crashes
Bibliographic record
Abstract
The National Automotive Sampling System/Crashworthiness Data System (NASS/CDS) remains the best US data source for understanding the magnitude of the opportunities for reducing rollover injuries to the various body regions. However, judicious analysis techniques are required to address the many confounding factors, including but not limited to the consequence of recent safety improvements such as electronic stability control and increased roof strength. To better assess the effect of recent safety improvements, the population of drivers in rollovers in light vehicles model year 2000 and later was examined. To address crash severity, the number of quarter-turns was used. Injuries were separated by body region and the HARM method of aggregating injuries was used to provide added weighting to the more severe injuries. For belted drivers in near-side rollovers, the fourth quarter-turn contained the most HARM and the highest injury risk, especially for chest injuries. For belted drivers in far-side rollovers, most of the chest injury HARM is fairly uniformly distributed between quarter-turns 2, 4, 6 and 8.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".