Risk of Motor Vehicle Collision or Driving Impairment After Traumatic Brain Injury: A Collaborative International Systematic Review and Meta-Analysis
Bibliographic record
Abstract
OBJECTIVE: To synthesize knowledge of the risk of motor vehicle collision (MVC) following a traumatic brain injury (TBI) and the associated risk of driving impairment, as measured by on-road tests, computerized simulators, and self-reported or state-recorded driving records. METHODS: Our international team searched 7 databases for studies published between 1990 and 2015 of people with TBI, controls, and data concerning either MVC or driving impairment. The included articles examined the risk of MVC among people with TBI; we excluded studies that examined the risk of having a TBI associated with being involved in an MVC. RESULTS: From 13 578 search results, we included 8 studies involving 1663 participants with TBI and 4796 controls. We found no significant difference in the risk of MVC (odds ratio = 1.24, 95% confidence interval = 0.80-1.91, P = .34). When we restricted the analysis to self-report, the risk of MVC was higher for those without a TBI (odds ratio = 1.63, 95% confidence interval = 1.21-2.22, P = .002). In contrast, participants with TBI consistently performed worse during on-road assessments and had more problems with vehicular control. CONCLUSION: Limitations of reviewed studies included small sample sizes, failure to specify TBI severity or time postinjury, and absence of objective measures of risk. Findings concerning the relationship between TBIs from non-MVC causes and crash risk are, therefore, inconclusive and do not provide evidence for major changes to existing clinical guidelines for driving with 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 | 0.017 | 0.062 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.036 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".