Hanging up the surgical cap: Assessing the competence of aging surgeons
Bibliographic record
Abstract
BACKGROUND: As the average age of surgeons continues to rise, determining when a surgeon should retire is an important public safety concern. AIM: To investigate strategies used to determine competency in the industrial workplace that could be transferrable in the assessment of aging surgeons and to identify existing competency assessments of practicing surgeons. METHODS: We searched websites describing non-medical professions within the United States where cognitive and physical competency are necessary for public safety. The mandatory age and certification process, including cognitive and physical requirements, were reported for each profession. Methods for determining surgical competency currently in use, and those existing in the literature, were also identified. RESULTS: Four non-medical professions requiring mental and physical aptitude that involve public safety and have mandatory testing and/or retirement were identified: Airline pilots, air traffic controllers, firefighters, and United States State Judges. Nine late career practitioner policies designed to evaluate the ageing physician, including surgeons, were described. Six of these policies included subjective performance testing, 4 using peer assessment and 2 using dexterity testing. Six objective testing methods for evaluation of surgeon technical skill were identified in the literature. All were validated for surgical trainees. Only Objective Structured Assessment of Technical Skills (OSATS) was capable of distinguishing between surgeons of different skill level and showing a relationship between skill level and post-operative outcomes. CONCLUSION: A surgeon should not be forced to hang up his/her surgical cap at a predetermined age, but should be able to practice for as long as his/her surgical skills are objectively maintained at the appropriate level of competency. The strategy of using skill-based simulations in evaluating non-medical professionals can be similarly used as part of the assessment of the ageing surgeons' surgical competency, showing who may require remediation or retirement.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".