Bibliographic record
Abstract
Some may remember an aging colleague who, we believed, stayed in practice far too long.We wondered whether their patients obtained optimal care in the twilight of their (surgeons') careers.Imagine, also that, your Division wanted to hire a new recruit.This individual would introduce modern skills.Due to limited hospital resources, the aging surgeon would need to vacate their position for the new recruit.The timing of retirement for some surgeons has been impacted by the unemployment or underemployment of recent graduates in Canada. 1 This editorial addresses the sensitive topic of retirement.It is usually discussed behind closed doors at the Heads of surgical services committee meetings.Discussion on retirement should not be a sensitive topic; it affects us all, and we have a collective responsibility to address it.By being proactive, plastic surgeons can avoid the consequences of a haphazard transition (Box 1).I will tackle the topic using a two-pronged fashion.First, I will summarize what the literature and law say about retirement for surgeons.Second, I will share my experience with transitioning to retirement.Hopefully, you will take something from this editorial that will be of use to you.Box 1. Consequences of a Haphazard Transition to Retirement.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.009 | 0.019 |
| Insufficient payload (model declined to judge) | 0.010 | 0.007 |
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".