Validity of the Mini‐Mental State Examination and the Montreal Cognitive Assessment in the Prediction of Driving Test Outcome
Bibliographic record
Abstract
OBJECTIVES: To evaluate the effectiveness of two cognitive screening measures, the Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA), in predicting driving test outcome for individuals with and without cognitive impairment. DESIGN: Retrospective cohort study. SETTING: A clinical driving evaluation program at a teaching hospital in the United States. PARTICIPANTS: Adult drivers who underwent assessment with the MMSE and MoCA as part of a comprehensive driving evaluation between 2010 and 2014 (N=92). MEASUREMENTS: MMSE and MoCA total scores were independent variables. The outcome measure was performance on a standardized road test. RESULTS: A preestablished diagnosis of cognitive impairment enhanced the validity of cognitive screening measures in the identification of at-risk drivers. In individuals with cognitive impairment there was a significant relationship between MoCA score and on-road outcome. Specifically, an individual was 1.36 times as likely to fail the road test with each 1-point decrease in MoCA score. No such relationship was detected in those without a diagnosis of cognitive impairment. CONCLUSION: For individuals who have not been diagnosed with cognitive impairment, neither the MMSE nor the MoCA can be reliably used as an indicator of driving risk, but for individuals with a preestablished diagnosis of cognitive impairment, the MoCA is a useful tool in this regard. A score on the MoCA of 18 or less should raise concerns about driving safety.
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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 | 0.011 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".