Understanding the effectiveness of different exercise training programme designs on VO<sub>2peak</sub>in COPD: a component network meta-analysis
Bibliographic record
Abstract
Pulmonary rehabilitation programmes including aerobic training improve cardiorespiratory fitness in patients with COPD, but the optimal programme design is unclear. We used random effects additive component network meta-analysis to investigate the relative effectiveness of different programme components on fitness measured by V̇O2peakin COPD. The included 59 studies involving 2191 participants demonstrated that V̇O2peakincreased after aerobic training of at least moderate intensity with the greatest improvement seen following high intensity training. Lower limb aerobic training (SMD 0.56 95% CI 0.32;0.81, intervention arms=86) and the addition of non-invasive ventilation (SMD 0.55 95% CI 0.04;1.06, intervention arms=4) appeared to offer additional benefit but there was limited evidence for effectiveness of other exercise and non-exercise components.
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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 | 0.020 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.043 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".