A Systematic Review and Meta-Analysis of On-Road Simulator and Cognitive Driving Assessment in Alzheimer’s Disease and Mild Cognitive Impairment
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Many individuals with Alzheimer's disease (AD) and mild cognitive impairment (MCI) are at an increased risk of driving impairment. There is a need for tools with sufficient validity to help clinicians assess driving ability. OBJECTIVE: Provide a systematic review and meta-analysis of the primary driving assessment methods (on-road, cognitive, driving simulation assessments) in patients with MCI and AD. METHODS: We investigated (1) the predictive utility of cognitive tests and domains, and (2) the areas and degree of driving impairment in patients with MCI and AD. Effect sizes were derived and analyzed in a random effects model. RESULTS: Thirty-two articles (including 1,293 AD patients, 92 MCI patients, 2,040 healthy older controls) met inclusion criteria. Driving outcomes included: On-road test scores, pass/fail classifications, errors; caregiver reports; real world crash involvement; and driving simulator collisions/risky behavior. Executive function (ES [95% CI]; 0.61 [0.41, 0.81]), attention (0.55 [0.33, 0.77]), visuospatial function (0.50 [0.34, 0.65]), and global cognition (0.61 [0.39, 0.83]) emerged as significant predictors of driving performance. Trail Making Test Part B (TMT-B, 0.61 [0.28, 0.94]), TMT-A (0.65 [0.08, 1.21]), and Maze test (0.88 [0.60, 1.15]) emerged as the best single predictors of driving performance. Patients with very mild AD (CDR = 0.5) mild AD (CDR = 1) were more likely to fail an on-road test than healthy control drivers (CDR = 0), with failure rates of 13.6%, 33.3% and 1.6%, respectively. CONCLUSION: The driving ability of patients with MCI and AD appears to be related to degree of cognitive impairment. Across studies, there are inconsistent cognitive predictors and reported driving outcomes in MCI and AD patients. Future large-scale studies should investigate the driving performance and associated neural networks of subgroups of AD (very mild, mild, moderate) and MCI (amnestic, non-amnestic, single-domain, multiple-domain).
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.002 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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