Surgical Cancelations and Postponements by Surgeon and Patient Sex
Bibliographic record
Abstract
OBJECTIVE: To estimate the association between surgeon sex with surgical postponements or cancelations. BACKGROUND: Female surgeons receive lower hourly, per-patient, and total compensation than their male colleagues. Bias in the decision to postpone or cancel surgical cases may contribute to compensation inequality, since this results in unpaid surgeon time. METHODS: This retrospective cohort study used administrative health data to identify surgeries performed at 4 hospitals in Calgary, Alberta, Canada, that were canceled or postponed due to surgeon/operating room overbooking or to accommodate an emergency case between April 1, 2015 and March 31, 2020. Surgeries performed in dedicated operating or procedure rooms (eg, bronchoscopy, cardiac surgery, etc) were excluded. The exposure of interest was surgeon sex, identified by matching their name to the provincial regulatory body record of self-identified sex, which allowed for selection between female and male only during the time of this study. RESULTS: There were 214,832 eligible surgical cases, of which 1481 and 2473 were postponed or canceled due to overbooking and to accommodate an emergency, respectively. After adjusting for surgical specialty, whether the procedure was a day case, and for patient sex, female surgeons were more likely to be canceled or postponed to accommodate an emergency case compared with male surgeons (odds ratio: 1.21, 95% CI: 1.05-1.38). CONCLUSIONS: There may be sex bias in the decision about which surgical cases to postpone or cancel to accommodate emergency surgeries in our setting. This bias may contribute to compensation inequality in a fee-for-service setting.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".