On-road assessment of fitness-to-drive in persons with MS with cognitive impairment: A prospective study
Bibliographic record
Abstract
BACKGROUND: Cognitive impairment is common in multiple sclerosis (MS). In other populations, cognitive impairment is known to affect fitness-to-drive. Few studies have focused on fitness-to-drive in MS and no studies have solely focused on the influence of cognitive impairment. OBJECTIVE: To assess fitness-to-drive in persons with MS with cognitive impairment and low physical disability. METHODS: Persons with MS, aged 18-59 years with EDSS ⩽ 4.0, impaired processing speed, and impairment on at least one measure of memory or executive function, were recruited. Cognition was assessed using the Minimal Assessment of Cognitive Function battery. A formal on-road driving assessment was conducted. Chi-square analysis examined the association between the fitness-to-drive (pass/fail) and the neuropsychological test results (normal/impaired). Bayesian statistics predicting failure of the on-road assessment were calculated. RESULTS: ( df = 1, N = 36) = 3.956; p = 0.047) with a sensitivity of 100%, but low specificity (35.7%) due to false positives (18/25). CONCLUSION: In persons with MS and impaired processing speed, impairment on the BVMTR-IR should lead clinicians to address fitness-to-drive.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".