When should surgeons retire?
Bibliographic record
Abstract
BACKGROUND: Retirement policies for surgeons differ worldwide. A range of normal human functional abilities decline as part of the ageing process. As life expectancy and their population increases, the performance ability of ageing surgeons is now a growing concern in relation to patient care. The aim was to explore the effects of ageing on surgeons' performance, and to identify current practical methods for transitioning surgeons out of practice at the appropriate time and age. METHODS: A narrative review was performed in MEDLINE using the terms 'ageing' and 'surgeon'. Additional articles were hand-picked. Modified PRISMA guidelines informed the selection of articles for inclusion. Articles were included only if they explored age-related changes in brain biology and the effect of ageing on surgeons' performance. RESULTS: The literature search yielded 1811 articles; of these, 36 articles were included in the final review. Wide variation in ability was observed across ageing individuals (both surgical and lay). Considerable variation in the effects of the surgeon's age on patient mortality and postoperative complications was noted. A lack of neuroimaging research exploring the ageing of surgeons' brains specifically, and lack of real markers available for measuring surgical performance, both hinder further investigation. Standard retirement policies in accordance with age-related surgical ability are lacking in most countries around the world. CONCLUSION: Competence should be assessed at an individual level, focusing on functional ability over chronological age; this should inform retirement policies for surgeons.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".