Preoperative quality of life predicts complications in thoracic surgery: a retrospective cohort study
Bibliographic record
Abstract
OBJECTIVES: Patients undergoing thoracic surgery experience high complication rates. It is uncertain whether preoperative health-related quality of life (HRQOL) measurements can predict patients at higher risk for postoperative complications. The objective of this study was to determine the association between preoperative HRQOL and postoperative complications among patients undergoing thoracic surgery. METHODS: This was a retrospective cohort study of prospectively collected data. Consecutive patients undergoing elective thoracic surgery at a Canadian tertiary care centre between January 2018 and January 2019 were included. Patient HRQOL was measured using the Euroqol-5 Dimension (EQ-5D) survey. Complications were recorded using the Ottawa Thoracic Morbidity and Mortality system. Uni- and multivariable analysis were performed. RESULTS: Of 515 surgeries performed, 133 (25.8%) patients experienced at least 1 postoperative complication; 345 (67.0%) patients underwent surgery for malignancy. A range of 271 (52.7%) to 310 (60.2%) patients experienced pain/discomfort at each timepoint. On multivariable analysis, lower preoperative EQ-5D visual analogue scale scores were significantly associated with postoperative complications (adjusted odds ratio 0.97, 95% confidence interval 0.95-0.99; P = 0.01). Presence of malignancy was not independently associated with complications (P = 0.68). CONCLUSIONS: Self-reported preoperative HRQOL can predict incidence of postoperative complications among patients undergoing thoracic surgery. Assessments of preoperative HRQOL may help identify patients at higher risk for developing complications. These findings could be used to direct preoperative risk-mitigation strategies in areas of HRQOL where patients suffer most, such as pain. The full perioperative trajectory of patient HRQOL should be discerned to identify subsets of patients who share common risk factors.
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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 | 0.014 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".