The aging anesthesiologist: a narrative review and suggested strategies
Bibliographic record
Abstract
PURPOSE: To address an aging anesthesia workforce, we review the relevant changes and implications associated with age in order to stimulate discussion at the individual, local, and national levels regarding appropriate changes in practice aimed at protecting patient safety. PRINCIPAL FINDINGS: In a 2013 survey of Canadian Anesthesiologists, 22% were aged 55-64 yr, 7% were aged 65-74 yr, and 3% were older than 74 yr. Clinical abilities decline with age, making older anesthesiologists more likely than their younger colleagues to be associated with adverse patient events. Anesthesiologists older than 65 yr in Ontario, Quebec, and British Columbia had 50% more cases involving litigation and almost twice the number of cases involving severe patient injury compared with anesthesiologists younger than 51 yr of age. In the absence of overt deterioration in skills, decisions about reducing activities and retirement are left largely to individuals despite their limited ability to self-assess competence. This state of affairs may contribute to the increased incidence of adverse events and poor patient outcomes. CONCLUSIONS: Provincial regulatory bodies have peer assessment programs to evaluate physicians at random, following a complaint, and at certain ages, but all have limitations. Simulation has been used widely for training and assessment in the aviation industry as well as in automobile driving exams. Simulation can assess crisis recognition and management, which is crucial in anesthesiology and not well assessed by other methods, and could assist elderly anesthesiologists during the pre-retirement phase of their careers. A standardized schedule for winding down would have advantages for physicians, their department, and their patients. A suggested schedule might include no further on-call duties for those aged 60 yr and older, no further high-acuity cases for those aged 65 yr and older, and retirement from operating room (OR) clinical practice (with possible continuation of non-OR clinical or other non-clinical activities, if desired) at age 70 yr. These timelines could be extended with satisfactory performance in annual simulation sessions involving assessment and practice in crisis management.
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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 | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.005 |
| 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".