Visual Testing for Readiness to Drive After Stroke
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to determine the ability of a visual-perception assessment tool, the Motor-Free Visual Perception Test, to predict on-road driving outcome in subjects with stroke. DESIGN: This was a retrospective study of 269 individuals with stroke who completed visual-perception testing and an on-road driving evaluation. Driving evaluators from six evaluation sites in Canada and the United States participated. Visual-perception was assessed using the Motor-Free Visual Perception Test. Scores range from 0 to 36, with a higher score indicating better visual perception. A structured on-road driving evaluation was performed to determine fitness to drive. Based on driving behaviors, a pass or fail outcome was determined by the examiner. RESULTS: The results indicated that, using a score on the Motor-Free Visual Perception Test of < or =30 to indicate poor visual-perception and >30 to indicate good visual perception, the positive predictive value of the Motor-Free Visual Perception Test in identifying those who would fail the on-road test was 60.9% (n = 67/110). The corresponding negative predictive value was 64.2% (n = 102/159). Univariate logistic regression analyses revealed that older age, low Motor-Free Visual Perception Test scores and a right hemisphere lesion contributed significantly to identifying those who failed the on-road test. CONCLUSIONS: The predictive validity of the Motor-Free Visual Perception Test is not sufficiently high to warrant its use as the sole screening tool in identifying those who are unfit to undergo an on-road evaluation.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".