Effects of Presurgical Exercise Training on Quality of Life in Patients Undergoing Lung Resection for Suspected Malignancy
Bibliographic record
Abstract
The aim of this study was to explore the effects of presurgical exercise training on quality of life (QOL) in patients with malignant lung lesions. Using a single-group prospective design, patients were enrolled in supervised aerobic exercise training for the duration of surgical wait time (mean 59.7 days). Participants completed assessments of cardiorespiratory fitness (peak oxygen consumption) and QOL using the Functional Assessment of Cancer Therapy-Lung scales, including the trial outcome index (TOI) and the lung cancer subscale (LCS) at baseline, immediately presurgery, and postsurgery (mean, 57 days). 9 participants provided complete data. Repeated-measures analysis indicated a significant effect for time on TOI (P = .006) and LCS (P = .009). Paired analysis revealed that QOL was unchanged after exercise training (ie, baseline to presurgery), but there were significant and clinically meaningful declines from presurgery to postsurgery in the LCS (-3.6, P = .021) and TOI (-8.3, P = .018). Change in peak oxygen consumption from presurgery to postsurgery was significantly associated with change in the LCS (r = 0.70, P = .036) and TOI (r = 0.70, P = .035). Exercise training did not improve QOL from baseline to presurgery. Significant declines in QOL after surgery seem to be related to declines in cardiorespiratory fitness. A randomized controlled trial is needed to further investigate these relationships.
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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 | 0.000 | 0.002 |
| 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) | 0.001 | 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".