Exercise testing and postoperative complications after minimally invasive lung resection: A cohort study
Bibliographic record
Abstract
Background: Peak oxygen uptake ( V˙O2 ) during cardiospulmonary exercise testing (CPET) is used to stratify postoperative risk following lung cancer resection but peak V˙O2 thresholds to predict post-operative mortality and morbidity were derived mostly from patients who underwent thoracotomy. Objectives: We evaluated whether peak V˙O2 or other CPET-derived variables predict post-operative mortality and cardiopulmonary morbidity after minimally invasive video-assisted thoracoscopic surgery (VATS) for lung cancer resection. Methods: A retrospective analysis of patients who underwent VATS lung resection between 2002 and 2019 and in whom CPET was performed. Logistic regression models were used to determine predictors of postoperative outcomes until 30 days after surgery. The ability of peak V˙O2 to discriminate between patients with and without post-operative complications was evaluated using Receiver operating characteristic (ROC) analysis. Results: Among the 593 patients, postoperative cardiopulmonary complications occurred in 92 (15.5%) individuals, including three deaths. Mean peak V˙O2 was 18.8 ml⋅kg−1⋅min−1, ranging from 7.0 to 36.4 ml⋅kg−1⋅min−1. Best predictors of postoperative morbidity and mortality were peripheral arterial disease, bilobectomy or pneumonectomy (versus sublobar resection), preoperative FEV1, peak V˙O2 , and peak V˙E/V˙CO2 . The proportion of patients with peak V˙O2 of < 15 ml⋅kg−1⋅min−1, 15 to < 20 ml⋅kg−1⋅min−1 and ≥ 20 ml⋅kg−1⋅min−1 experiencing at least one postoperative complication was 23.8, 16.3 and 10.4%, respectively. The area under the ROC curve for peak V˙O2 was 0.63 (95% CI: 0.57–0.69). Conclusion: Although lower peak V˙O2 was a predictor of postoperative complications following VATS lung cancer resection, its ability to discriminate patients with or without complications was limited.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".