Crash Risk Following Return to Driving After Moderate-to-Severe TBI: A TBI Model Systems Study
Bibliographic record
Abstract
OBJECTIVE: To examine motor vehicle crash frequency and risk factors following moderate-to-severe traumatic brain injury (TBI). SETTING: Eight TBI Model Systems sites. Participants: Adults ( N = 438) with TBI who required inpatient acute rehabilitation. DESIGN: Cross-sectional, observational design. MAIN MEASURES: Driving survey completed at phone follow-up 1 to 30 years after injury. RESULTS: TBI participants reported 1.5 to 2.5 times the frequency of crashes noted in the general population depending on the time frame queried, even when accounting for unreported crashes. Most reported having no crashes; for those who experienced a crash, half of them reported a single incident. Based on logistic regression, age at survey, years since injury, and perception of driving skills were significantly associated with crashes. CONCLUSION: Compared with national statistics, crash risk is higher following TBI based on self-report. Older age and less time since resuming driving were associated with lower crash risk. When driving was resumed was not associated with crash risk. These results do not justify restricting people from driving after TBI, given that the most who resumed driving did not report experiencing any crashes. However, there is a need to identify and address factors that increase crash risk after TBI.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".