Enhancing guidelines for managing cognitively impaired drivers: Insights from Western evidence for Asian adaptation
Bibliographic record
Abstract
Introduction: The global incidence of dementia is increasing, and cognitively impaired drivers are at a higher risk of crashes compared to healthy drivers. Doctors face challenges in assessing these at-risk drivers, with questionable adherence to existing guidelines. This study aimed to review and compare guidelines for managing cognitively impaired drivers from various countries. Method: A scoping review was conducted to identify relevant guidelines, which were then descriptively compared with Singapore's guideline. Results: Eleven guidelines from 8 countries: US (n=2), Canada (n=2), UK (n=2), Ireland, Belgium, Australia, New Zealand and Singapore were reviewed. All guidelines support driving assessments and conditional licensing in ordinary (i.e. non-professional) drivers with dementia. Canada stands out for not allowing co-piloting and geographical restrictions in conditional licensing practice. Few guidelines provide indemnity for doctors reporting to licensing authorities, and communication about the impact of dementia on car insurance is rarely addressed. Most Western guidelines include evidence-based approaches, provisions for drivers with mild cognitive impairment and early discussions on transitioning from driving. A clinic-based functional screening toolbox and 2 clinical algorithms (1 with and 1 without the Clinical Dementia Rating scale) were identified as having universal applicability. Singapore's guideline, by comparison, is outdated and lacks both developmental rigour and guidance on managing mild cognitive impairment and transitioning drivers out of driving. Conclusion: Comprehensive, evidence-based guidelines from Western countries provide valuable resources that can help Singapore design or update its guideline.
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How this classification was reachedexpand
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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".