The effect of multimodal prehabilitation on postoperative outcomes in lung cancer surgery
Bibliographic record
Abstract
OBJECTIVE: Patients with lung cancer are often elderly, frail, and smokers with poor functional reserve, making them excellent candidates for multimodal prehabilitation to improve postoperative outcomes. Patients referred to the prehabilitation clinic are at an even higher surgical risk. This retrospective observational study aimed to compare the postoperative 30-day outcomes in lung cancer surgery among the propensity score-matched patients. METHODS: Patients who underwent lung cancer surgery between August 2018 and January 2024 were accessed for eligibility. After exclusion, a 1:1 propensity score-matching analysis was performed based on the following baseline characteristics: respiratory disease, predicted length of stay based on American College of Surgeons National Surgical Quality Improvement Program, Duke Activity Status Index less than 34, tumor stage, and neoadjuvant therapy. Baseline characteristics, preoperative and intraoperative data, and postoperative outcomes were compared between the matched patients. RESULTS: Among 1242 patients, 555 were selected for propensity score matching, resulting in 147 matched pairs in each group. The control group exhibited significantly higher rates of overall (65.3% vs 46.3%, P = .001) and major complications (27.9% vs 13.6%, P = .003). Patients who underwent multimodal prehabilitation had a significantly lower Comprehensive Complication Index (12.2 [0-26.2] vs 0 [0-20.9], P < .0001), reduced intensive care unit admission rates (8.2% vs 2.7%, P = .040), and lower readmission rates (14.3% vs 6.1%, P = .021). CONCLUSIONS: Multimodal prehabilitation significantly reduced overall and major postoperative 30-day complications in lung cancer surgery. It also contributed to reducing the severity of complications. These findings suggest that multimodal prehabilitation may improve postoperative 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".