In-office evaluation of medical fitness to drive: practical approaches for assessing older people.
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
OBJECTIVE: To provide background for physicians'in-office assessment of medical fitness to drive, including legal risks and responsibilities. To review opinion-based approaches and current attempts to promote evidence-based strategies for this assessment. QUALITY OF EVIDENCE: MEDLINE, EMBASE, CINAHL, PsyclNFO, Ageline, and Sociofile were searched from 1966 on for articles on health-related and medical aspects of fitness to drive. More than 1500 papers were reviewed to find practical approaches to, or guidelines for, assessing medical fitness to drive in primary care. Only level III evidence was found. No evidence-based approaches were found. MAIN MESSAGE: Three practical methods of assessment are discussed: the American Medical Association guidelines, SAFE DRIVE, and CanDRIVE. CONCLUSION: There is no evidence-based information to help physicians make decisions regarding medical fitness to drive. Current approaches are primarily opinion-based and are of unknown predictive value. Research initiatives, such as the CanDRIVE program of the Canadian Institutes of Health Research, can provide empiric data that would allow us to move from opinion to evidence.
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 | 0.008 | 0.013 |
| 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