Evaluation and Management of the Driver with Dementia
Bibliographic record
Abstract
BACKGROUND: The number of older adult drivers with dementia is expected to increase over the next few decades. This increase raises public and personal safety concerns given the higher crash rates of drivers with a dementing illness. However, the identification of drivers with a dementia who may be at risk for a crash is difficult, particularly for those in the early stages of dementia. REVIEW SUMMARY: Studies examining the correlation of dementia with driving outcomes such as motor vehicle crashes are reviewed. The strengths and weaknesses of recent consensus statements, published to assist clinicians in evaluating drivers with a dementia, are discussed. The authors also review common practices currently in use by physicians to identify at-risk drivers, including mental status examinations, global dementia rating scales, specialist referral, medical evaluations, and the use of caregiver reports and other proxy measures. Legal issues, based on the role of the physician, are reviewed along with suggestions for driving cessation and education for the caregiver and family. CONCLUSIONS: In patients with mild to moderate dementia, the literature indicates that physicians would have difficulty in identifying which individuals should not drive. Performance-based measures of driving skills, such as on-road driving tests, are recommended as a means of assessing driving competency.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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 | 0.001 |
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".