Toward late career transitioning: a proposal for academic surgeons
Bibliographic record
Abstract
SUMMARY: In the absence of a defined retirement age, academic surgeons need to develop plans for transition as they approach the end of their academic surgical careers. The development of a plan for late career transition represents an opportunity for departments of surgery across Canada to initiate a constructive process in cooperation with the key stakeholders in the hospital or institution. The goal of the process is to develop an individual plan for each faculty member that is agreeable to the academic surgeon; informs the surgical leadership; and allows the late career surgeon, the hospital, the division and the department to make plans for the future. In this commentary, the literature on the science of aging is reviewed as it pertains to surgeons, and guidelines for late career transition planning are shared. It is hoped that these guidelines will be of some value to academic programs and surgeons across the country as late career transition models are developed and adopted.
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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 | 0.059 | 0.116 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.017 | 0.019 |
| Scholarly communication | 0.017 | 0.017 |
| Open science | 0.008 | 0.015 |
| Research integrity | 0.067 | 0.059 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".