The Mini‐Mental State Examination, Clinical Factors, and Motor Vehicle Crash Risk
Bibliographic record
Abstract
OBJECTIVES: To examine the association between Mini-Mental State Examination (MMSE) score and motor vehicle crash (MVC) risk in a large cohort of community-dwelling participants with cardiovascular disease (CVD) or diabetes mellitus. DESIGN: Prospective observational study. SETTING: Participants enrolled in the Ongoing Telmisartan Alone and in Combination With Ramipril Global End Point Trial and Telmisartan Randomized Assessment Study in ACE Intolerant Subjects with Cardiovascular Disease clinical trial, which included individuals aged 55 and older with CVD or diabetes mellitus. PARTICIPANTS: Totally 17,538 frequent drivers (defined as driving at least once per week) who had completed a baseline MMSE. MEASUREMENTS: Involvement in a MVC as the driver. RESULTS: Baseline MMSE score was divided into four categories: 30, 27-29, 24-26, and <24. The median MMSE score was 29 (interquartile range 27-30), and 726 (4.1%) has a MMSE score of less than 24 at baseline. After a mean follow-up of 4.5 years, 1,068 (6.1%) participants were drivers in a MVC. Lower scores were not associated with future MVCs (MMSE score 29-27, hazard ratio (HR)=1.06, 95% confidence interval (CI)=0.93-1.22); MMSE score 26-24, HR=0.96, 95% CI=0.78-1.19; MMSE score<24, HR=0.72, 95% CI=0.50-1.05) on multivariable analysis. A MVC within the previous 2 years (HR=2.68, 95% CI=2.29-3.13) was the strongest predictor of future MVCs. Other clinical factors associated with greater MVC risk were depression, falls within the previous year, sleep apnea, and lower baseline systolic blood pressure. CONCLUSION: In a population of frequent drivers, the MMSE does not predict MVCs. Other clinical factors have a stronger association with MVC risk.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".