An international study of the quality of national-level guidelines on driving with medical illness
Bibliographic record
Abstract
BACKGROUND: Medical illnesses are associated with a modest increase in crash risk, although many individuals with acute or chronic conditions may remain safe to drive, or pose only temporary risks. Despite the extensive use of national guidelines about driving with medical illness, the quality of these guidelines has not been formally appraised. AIM: To systematically evaluate the quality of selected national guidelines about driving with medical illness. DESIGN: A literature search of bibliographic databases and Internet resources was conducted to identify the guidelines, each of which was formally appraised. METHODS: Eighteen physicians or researchers from Canada, Australia, Ireland, USA and UK appraised nine national guidelines, applying the Appraisal of Guidelines for Research and Evaluation (AGREE II) instrument. RESULTS: Relative strengths were found in AGREE II scores for the domains of scope and purpose, stakeholder involvement and clarity of presentation. However, all guidelines were given low ratings on rigour of development, applicability and documentation of editorial independence. Overall quality ratings ranged from 2.25 to 5.00 out of 7.00, with modifications recommended for 7 of the guidelines. Intra-class coefficients demonstrated fair to excellent appraiser agreement (0.57-0.79). CONCLUSIONS: This study represents the first systematic evaluation of national-level guidelines for determining medical fitness to drive. There is substantive variability in the quality of these guidelines, and rigour of development was a relative weakness. There is a need for rigorous, empirically derived guidance for physicians and licensing authorities when assessing driving in the medically ill.
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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 | 0.194 | 0.560 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.015 | 0.021 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".