Impact of surgeon and anaesthesiologist sex on patient outcomes after cardiac surgery: a population-based study
Bibliographic record
Abstract
<b>Background: </b>Effective teamwork between anaesthesiologists and surgeons is essential for optimising patient safety in the cardiac operating room. While many factors may influence the relationship between these two physicians, the role of sex and gender have yet to be investigated. <b>Objectives: </b>We sought to determine the association between cardiac physician team sex discordance and patient outcomes. <b>Design: </b>We performed a population-based, retrospective cohort study. <b>Participants and setting: </b>Adult patients who underwent coronary artery bypass grafting (CABG) and/or aortic, mitral or tricuspid valve surgery between 2008 and 2018 in Ontario, Canada. <b>Primary and secondary outcome measures: </b>The primary outcome was all-cause 30-day mortality. Secondary outcomes included major adverse cardiovascular events at 30 days and hospital and intensive care unit lengths of stay (LOS). Mixed effects logistic regression was used for categorical outcomes and Poisson regression for continuous outcomes. <b>Results: </b>79 862 patients underwent cardiac surgery by 98 surgeons (11.2% female) and 279 anaesthesiologists (23.3% female); 19 893 (24.9%) were treated by sex-discordant physician teams. Physician sex discordance was not associated with overall patient mortality or LOS; however, patients who underwent isolated CABG experienced longer hospital LOS when treated by an all-male physician team as compared with an all-female team (adjusted OR=1.07; p=0.049). When examining the impact of individual physician sex, the length of hospital stay was longer when isolated CABG procedures were attended by a male surgeon (OR=1.10; p=0.004) or anaesthesiologist (OR=1.02; p=0.01). <b>Conclusions: </b>Patient mortality and length of stay after cardiac surgery may vary by sex concordance of the attending surgeon-anaesthesiologist team. Further research is needed to examine the underlying mechanisms of these observed relationships.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".