Return to Driving Following Moderate-to-Severe Traumatic Brain Injury: A TBI Model System Longitudinal Investigation
Bibliographic record
Abstract
OBJECTIVE: To examine longitudinal patterns of return to driving (RTD), driving habits, and crash rates associated with moderate-to-severe traumatic brain injury (TBI). SETTING: Eight TBI Model System sites. PARTICIPANTS: Adults ( N = 334) with TBI that required inpatient acute rehabilitation with follow-up of 197 and 218 at 1 and 2 years post-injury, respectively. Data collection at 2 years occurred almost exclusively during the pandemic, which may have affected results. DESIGN: Longitudinal and observational. MAIN MEASURES: Driving survey completed during rehabilitation and at phone follow-up 1 and 2 years after injury. RESULTS: The rate of RTD was 65% at 1-year follow-up and 70% at 2-year follow-up. RTD at both follow-up time points was positively associated with family income. The frequency of driving and distance driven were diminished compared to before injury. Limitation of challenging driving situations (heavy traffic, bad weather, and at night) was reported at higher rates post-injury than before injury. Crash rates were 14.9% in the year prior to injury (excluding crashes that resulted in TBI), 9.9% in the first year post-injury, and 6% during the second year. CONCLUSION: RTD is common after TBI, although driving may be limited in terms of frequency, distance driven, and avoiding challenging situations compared to before injury. Incidence of crashes is higher than population-based statistics; however, those who sustain TBI may be at higher risk even prior to injury. Future work is needed to better identify characteristics that influence the likelihood of crashes post-TBI.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".