Approach to assessing fitness to drive in patients with cardiac and cognitive conditions.
Bibliographic record
Abstract
OBJECTIVE: To help physicians become more comfortable assessing the fitness to drive of patients with complex cardiac and cognitive conditions. QUALITY OF EVIDENCE: The approach described is based on the authors' clinical practices, recommendations from the Third Canadian Consensus Conference on Diagnosis and Treatment of Dementia, and guidelines from the 2003 Canadian Cardiovascular Society Consensus Conference. MAIN MESSAGE: When assessing fitness to drive in patients with multiple, complex health problems, physicians should divide conditions that might affect driving into acute intermittent (ie, not usually present on examination) and chronic persistent (ie, always present on examination) medical conditions. Physicians should address acute intermittent conditions first, to allow time for recovery from chronic persistent features that might be reversible. Decisions regarding fitness to drive in acute intermittent disorders are based on probability of recurrence; decisions in chronic persistent disorders are based on functional assessment. CONCLUSION: Assessing fitness to drive is challenging at the best of times. When patients have multiple comorbidities, assessment becomes even more difficult. This article provides clinicians with systematic approaches to work through such complex cases.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".