Evaluation and Management of the Driver with Dementia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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