Should we screen aging physicians for cognitive decline?
Bibliographic record
Abstract
OBJECTIVES: To synthesize evidence relevant for informed decisions concerning cognitive testing of older physicians. METHODS: Relevant literature was systematically searched in Medline, EMBASE, PsycInfo, and ERIC, with key findings abstracted and synthesized. RESULTS: Cognitive abilities of physicians may decline in an age range where they are still practicing. Physician competence and clinical performance may also decline with age. Cognitive scores are lower in physicians referred for assessment because of competency or performance concerns. Many physicians do not accurately self-assess and continue to practice despite declining quality of care; however, perceived cognitive decline, although not an accurate indicator of ability, may accelerate physicians' decision to retire. Physicians are reluctant to report colleagues' cognitive problems. Several issues should be considered in implementing cognitive screening. Most cognitive assessment tools lack normative data for physicians. Scientific evidence linking cognitive test results with physician performance is limited. There is no known level of cognitive decline at which a doctor is no longer fit to practice. Finally, relevant domains of cognitive ability vary across medical specialties. CONCLUSION: Physician cognitive decline may impact clinical performance. If cognitive assessment of older physicians is to be implemented, it should consider challenges of cognitive test result interpretation.
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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 | 0.011 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".