Medico‐legal cases associated with older physicians’ cognitive ability to practice medicine
Bibliographic record
Abstract
BACKGROUND: Dementia increases as individuals age. Aging physicians represent a growing population. Studies have demonstrated there are physicians with cognitive impairments practicing medicine. The medico-legal consequences of physicians with cognitive impairments have not been investigated. METHODS: The Canadian Medical Protective Association (CMPA) is a national medical association with 108,000 members who advise and assist doctors with medico-legal matters. They maintain a national repository of legal actions and complaints to regulatory bodies and hospitals. We looked at civil-legal and regulatory college cases closed over a 10-year period associated with physicians aged ≥55. A word search of the cases was conducted using "Dementia, Alzheimer, Cognitive impairment, Cognitive decline, Memory loss, Memory issues, Fit for/to practice." RESULTS: The CMPA closed 67,566 cases between 2012 and 2021 and 16% (10,599) involved members ≥55. A mixed methodology approach identified 65 cases associated with physician's cognitive ability to practice medicine. Of these 65 cases, the average age of physician was 71.3 (56.1-88.5). The proportion of cases where concern was associated with a physician's cognitive ability to practice medicine increased, from 0.2% of cases in 55-60-year-olds, to 7.7% in physicians over 80. INTERPRETATION: As physicians age, concerns about cognitive impairment are more likely to contribute to medico-legal matters.
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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 | 0.005 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| 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 | 0.000 |
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".