Perioperative Outcomes of Lung Cancer Surgery in Women: A Canadian Nationwide Retrospective Cohort Study
Bibliographic record
Abstract
OBJECTIVES: Sex differences in perioperative outcomes following lung cancer surgery remain understudied. This study evaluated these differences in a national cohort. METHODS: Data for patients who underwent lung cancer surgery between January 2017-December 2022 at 13 hospitals were extracted from the Canadian Association of Thoracic Surgeons National Database. Preoperative characteristics, surgery-related, tumour-related, and postoperative outcomes data were collected. Mixed-effects logistic regression models were used to determine perioperative variables associated with female sex. RESULTS: A total of 9922 patients were included, and 55.4% were female. Female patients had higher rates of minor complications, lower rates of major complications, and lower mortality. Females were less likely to be active smokers (odds ratio [OR] = 0.66; 95% confidence interval [CI], 0.52, 0.83), have comorbidities, have squamous cell carcinoma (OR = 0.34; 95% CI, 0.26, 0.44), or an air leak complication postoperatively (OR = 0.66; 95% CI, 0.51, 0.86). However, females with chronic obstructive pulmonary disease (COPD) or squamous cell carcinoma had higher odds of experiencing an air leak complication postoperatively. CONCLUSIONS: Females had fewer comorbidities, less advanced stage cancer, less pulmonary resection, and different tumour types, all leading to lower rates of major complications and mortality compared to males. Understanding preoperative factors that contribute to sex differences in adverse events can enhance the short- and long-term outcomes for patients with lung cancer.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".