Understanding age-triggered cognitive assessments of late-career physicians
Bibliographic record
Abstract
Late-career physicians now represent a significant part of the physician workforce in the United States. The American Medical Association Council on Medical Education tracks physician demographic data and found that in 1975 there were 50,993 practicing physicians 65 years or older, but by 2013, this number had risen to 241,641 physicians, a 374% increase. The AMA Council also concluded that aging was associated with decreased processing speed, increased difficulty inhibiting irrelevant information, reduced hearing and visual acuity, decreased manual dexterity and visuospatial ability. There is mounting concern that the effects of aging can adversely impact the practice of medicine by late-career physicians. Although results are mixed, studies suggest late-career physicians have a higher rate of disciplinary action, fail to acquire new knowledge and have greater variability in test scores and their patients experience higher mortality rates after complex surgical procedures. Hospital administrators in their efforts to assess cognition of their aging medical staff are limited by the absence of validated metrics when it comes to older individuals with above-average years of education. Also, attempts to curtail medical practice based on age are fraught with legal implications arising from the Americans with Disabilities Act of 1990 and the Age Discrimination in Employment Act of 1967. We examined the issues hospital administrators face when formulating policies regulating the medical practice of late-career physicians. Our review summarizes the state of the literature of late-career physicians, reviews the legal implications of policies regarding age and the practice of medicine and offers our experience in creating a late-career physician policy for a multi-disciplinary medical staff.
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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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".