A systematic review of the risk of motor vehicle collision after stroke or transient ischemic attack
Bibliographic record
Abstract
BACKGROUND: Returning to driving after stroke is one of the key goals in stroke rehabilitation, and fitness to drive guidelines must be informed by evidence pertaining to risk of motor vehicle collision (MVC) in this population. OBJECTIVES: The purpose of the present study was to determine whether stroke and/or transient ischemic attack (TIA) are associated with an increased MVC risk. METHODS: We searched MEDLINE, CINAHL, EMBASE, PsycINFO, and TRID through December 2016. Pairs of reviewers came to consensus on inclusion, based on an iterative review of abstracts and full-text manuscripts, on data extraction, and on the quality of evidence. RESULTS: Reviewers identified 5,605 citations, and 12 articles met inclusion criteria. Only one of three case-control studies showed an association between stroke and MVC (OR 1.9, 95% CI 1.0-3.9). Of five cohort reports, only one study, limited to self-report, found an increased risk of MVC associated with stroke or TIA (RR 2.71, 95% CI 1.11-6.61). Two of four cross-sectional studies using computerized driving simulators identified a more than two-fold risk of MVCs among participants with stroke compared with controls. The difference in one of the studies was restricted to those with middle cerebral artery stroke. CONCLUSIONS: The evidence does not support a robust increase in risk of MVCs. While stroke clearly prevents some patients from driving at all and impairs driving performance in others, individualized assessment and clinical judgment must continue to be used in assessing and advising those stroke patients who return to driving about their MVC risk.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".