Effects of presurgical exercise training on cardiorespiratory fitness among patients undergoing thoracic surgery for malignant lung lesions
Bibliographic record
Abstract
BACKGROUND: To determine the effects of preoperative exercise training on cardiorespiratory fitness in patients undergoing thoracic surgery for malignant lung lesions. METHODS: Using a single-group design, 25 patients with suspected operable lung cancer were provided with structured exercise training until surgical resection. Exercise training consisted of 5 endurance cycle ergometry sessions per week at intensities varying from 60% to 100% of baseline peak oxygen consumption (VO(2 peak)). Participants underwent cardiopulmonary exercise testing, 6-minute walk (6 MW), and pulmonary function testing at baseline, immediately before, and 30 days after surgical resection. RESULTS: Five patients were deemed ineligible before surgical resection and were removed from the analysis. Of the remaining 20 patients follow-up assessments were obtained for 18 (90%) before resection and 13 (65%) patients postresection. The overall adherence rate was 72%. Intention-to-treat analysis indicated that mean VO(2peak) increased by 2.4 mL . kg(-1) . min(-1)(95% confidence interval [CI], 1.0-3.8; P = .002) and 6MW distance increased 40 m (95% CI, 16-64; P = .003) baseline to presurgery. Per protocol analyses indicated that patients who attended >or=80% of prescribed sessions increased VO(2peak) and 6 MWD by 3.3 mL.kg(-1).min(-1) (95% CI, 1.1-5.4; P = .006) and 49 meters (95% CI, 12-85; P = .013), respectively. Exploratory analyses indicated that presurgical exercise capacity decreased postsurgery, but did not decrease beyond baseline values. CONCLUSIONS: Preoperative exercise training is a beneficial intervention to improve cardiorespiratory fitness in patients undergoing pulmonary resection. This benefit may have important implications for surgical outcome and postsurgical recovery in this population. Larger randomized controlled trials are warranted.
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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 | 0.001 | 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".