Visual Testing for Readiness to Drive After Stroke
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 it