Evidence Based Review of Fitness-to-Drive and Return-to-Driving Following Traumatic Brain Injury
Bibliographic record
Abstract
The purpose of this study was to conduct an evidence-based review to determine predictors of fitness to drive and return to driving in persons with traumatic brain injury (TBI). Relevant databases (MEDLINE/PubMed, CINAHL, Cochrane Library, and SCOPUS) were searched for primary articles published before June 2016 using MeSH search terms. Using the American Academy of Neurology’s classification criteria, 24 articles were included after reviewing 1998 articles. Studies were rated by class (I–IV), with I being the highest level of evidence. Articles were classified according to TBI severity, as well as types of assessments (on-road, simulator and surveys). There were no Class I studies. Based on Class II studies, only Post-traumatic amnesia (PTA) duration was found to be probably predictive of on-road driving performance. There is limited evidence concerning predictors of return to driving. The findings suggest further evidence is needed to identify predictors of on-road driving performance in persons with TBI. Class I studies reporting Level A recommendations for definitive predictors of driving performance in drivers with TBI are needed by policy makers and clinicians to develop evidence-based guidelines.
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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 | 0.007 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".