Effect of Exercise Training on Peak Oxygen Consumption in Patients with Cancer: A Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: We conducted a meta-analysis to determine the effects of supervised exercise training on peak oxygen consumption (VO(2peak)) in adults with cancer. METHODS: A literature review using Ovid MEDLINE (1950-2010), the Cochrane Central Register of Controlled Trials (1991-2010), AMED (1985-2010), Embase (1988-2010), PubMed (1966-2010), Scopus (1950-2010), and Web of Science (1950-2010) was performed to identify randomized controlled trials examining the effects of supervised exercise training on measurement of VO(2peak) (via gas exchange analysis) in adults with cancer. Studies were selected using predetermined criteria, and two independent reviewers extracted data. Weighted mean differences (WMDs) were calculated using random effect models. RESULTS: Six studies evaluated VO(2peak) involving a total of 571 adult cancer patients (exercise, n = 344; usual care control, n = 227). Pooled data indicated that exercise training was associated with a statistically significant increase in VO(2peak) (WMD, 2.90 ml·kg(-1)·min(-1); 95% confidence interval [CI], 1.16-4.64); however, significant heterogeneity was evident in this estimate (I(2), 87%). Usual care (control) was associated with a significant decline in VO(2peak) from baseline to postintervention (WMD, -1.02 ml·kg(-1)·min(-1); 95% CI, -1.46 to -0.58; I(2), 22%). Sensitivity analyses indicated superior improvements in VO(2peak) for studies conducted for a shorter duration (<4 months) and following the completion of adjuvant therapy (p-values < .001). Exercise training was not associated with a higher incidence of adverse events, although safety was not rigorously monitored or reported. CONCLUSIONS: Supervised exercise training is associated with significant improvements in VO(2peak) following a diagnosis of early-stage cancer, with minimal adverse events.
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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 | 0.018 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.062 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".