Safety and feasibility of aerobic training on cardiopulmonary function and quality of life in postsurgical nonsmall cell lung cancer patients
Bibliographic record
Abstract
BACKGROUND: A feasibility study examining the effects of supervised aerobic exercise training on cardiopulmonary and quality of life (QOL) endpoints among postsurgical nonsmall cell lung cancer (NSCLC) patients was conducted. METHODS: Using a single-group design, 20 patients with stage I-IIIB NSCLC performed 3 aerobic cycle ergometry sessions per week at 60% to 100% of peak workload for 14 weeks. Peak oxygen consumption (VO(2peak)) was assessed using an incremental exercise test. QOL and fatigue were assessed using the Functional Assessment of Cancer Therapy-Lung (FACT-L) scale. RESULTS: Nineteen patients completed the study. Intention-to-treat analysis indicated that VO(2peak) increased 1.1 mL/kg(-1)/min(-1) (95% confidence interval [CI], -0.3-2.5; P = .109) and peak workload increased 9 W (95% CI, 3-14; P = .003), whereas FACT-L increased 10 points (95% CI, -1-22; P = .071) and fatigue decreased 7 points (95% CI; -1 to -17; P = .029) from baseline to postintervention. Per protocol analyses indicated greater improvements in cardiopulmonary and QOL endpoints among patients not receiving adjuvant chemotherapy. CONCLUSIONS: This pilot study provided proof of principle that supervised aerobic training is safe and feasible for postsurgical NSCLC patients. Aerobic exercise training is also associated with significant improvements in QOL and select cardiopulmonary endpoints, particularly among patients not receiving chemotherapy. Larger randomized trials are warranted.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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".