Relation between surgeon age and postoperative outcomes: a population-based cohort study
Bibliographic record
Abstract
BACKGROUND: Aging may detrimentally affect cognitive and motor function. However, age is also associated with experience, and how these factors interplay and affect outcomes following surgery is unclear. We sought to evaluate the effect of surgeon age on postoperative outcomes in patients undergoing common surgical procedures. METHODS: We performed a retrospective cohort study of patients undergoing 1 of 25 common surgical procedures in Ontario, Canada, from 2007 to 2015. We evaluated the association between surgeon age and a composite outcome of death, readmission and complications. We used generalized estimating equations for analysis, accounting for relevant patient-, procedure-, surgeon- and hospital-level factors. RESULTS: We found 1 159 676 eligible patients who were treated by 3314 surgeons and ranged in age from 27 to 81 years. Modelled as a continuous variable, a 10-year increase in surgeon age was associated with a 5% relative decreased odds of the composite outcome (adjusted odds ratio [OR] 0.95, 95% confidence interval [CI] 0.92 to 0.98, p = 0.002). Considered dichotomously, patients receiving treatment from surgeons who were older than 65 years of age had a 7% lower odds of adverse outcomes (adjusted OR 0.93, 95% CI 0.88–0.97, p = 0.03; crude absolute difference = 3.1%). INTERPRETATION: We found that increasing surgeon age was associated with decreasing rates of postoperative death, readmission and complications in a nearly linear fashion after accounting for patient-, procedure-, surgeon- and hospital-level factors. Further evaluation of the mechanisms underlying these findings may help to improve patient safety and outcomes, and inform policy about maintenance of certification and retirement age 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".