Predictive validity of the Montreal Cognitive Assessment (MoCA) as a screening tool for on-road driving performance
Bibliographic record
Abstract
Introduction The objectives of this study are to determine (1) the ability of the Montreal Cognitive Assessment to predict on-road driving performance in drivers with a neurological condition and elderly drivers with suspected cognitive decline, and (2) the association between the performance on the Useful Field of View and the performance on the Montreal Cognitive Assessment. Method This study used a retrospective design. Clients were included who had completed the Montreal Cognitive Assessment and the on-road driving evaluation from November 2006 to May 2009 ( n = 154) in a driving rehabilitation program in the Montreal Area. Total scores on the Montreal Cognitive Assessment, Useful Field of View risk categories, pass or fail outcomes from an on-road evaluation, as well as demographic and clinical characteristics were recorded from participants’ medical charts. Results The Montreal Cognitive Assessment was found to have a sensitivity of 84.5% and a specificity of 50% with a cut-off of ≤25. It was significantly associated with the Useful Field of View risk category. Conclusion The Montreal Cognitive Assessment could be a valuable screening tool. However, its predictive validity is not strong enough to recommend its use as the sole instrument for identifying unfit drivers.
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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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".