Cervical vertebral and spinal cord injuries in rollover occupants
Bibliographic record
Abstract
BACKGROUND: Rollover crashes continue to be a substantial public health issue in North America. Previous research has shown that the cervical spine is the most injured spine segment in rollovers, but much of the past research has focused on risk factors rather than the actual cervical spine injuries. We sought to examine how different types of cervical spine injuries (vertebral and/or cord injury) vary with different occupant-related factors in rollovers and to compare these with non-rollovers. METHODS: We obtained crash and injury information from the National Automotive Sampling System-Crashworthiness Data System (NASS-CDS) for 2005-2015 and Crash Investigation Sampling System (CISS) for 2017-2022. Based on weighted data, we calculated relative risks to assess how occupant sex, seat belt use, ejection status, and fatal outcome relate to the rate of different cervical spine injuries in rollovers and non-rollovers. RESULTS: In NASS-CDS occupants with cervical spine injuries (N = 111,040 weighted cases), about 91.5% experienced at least one vertebral injury whereas only 11.3% experienced a spinal cord injury (most of which had a concomitant vertebral fracture). All types of cervical spine injuries we examined were 3.4-5.2 times more likely to occur in rollovers compared to non-rollovers. These relative risks were similar for both sexes, belted and unbelted, non-ejected, and non-fatal occupants. The number of weighted CISS occupants with cervical spine injuries (N = 42,003) was smaller than in the NASS analysis, but cervical spine injuries remained 6.25 to 6.36 times more likely in rollovers compared to non-rollovers despite a more modern vehicle fleet. CONCLUSIONS: These findings underscore the continued need for rollover-specific safety countermeasures, especially those focused on cervical spine injury prevention, and elucidate the frequency, severity and other characteristics of the specific vertebral and spinal cord injuries being sustained in rollovers. Our findings suggest that countermeasures focused on preventing cervical vertebral fractures will also effectively prevent most cervical spinal cord injuries.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".