The effects of pulmonary rehabilitation on inflammatory biomarkers in patients with chronic obstructive pulmonary disease: A systematic review and meta-analysis
Bibliographic record
Abstract
Chronic obstructive pulmonary disease (COPD) is associated with elevated pro-inflammatory biomarkers which may contribute to changes in the respiratory and extrapulmonary systems. Pulmonary rehabilitation (PR) is standard care for people with COPD. The impact of PR on systemic inflammation is currently unknown. We conducted a systematic review following the Cochrane Handbook using the search concepts COPD, PR, and inflammatory biomarkers. We searched 5 databases and completed study screening and selection independently and in duplicate using Covidence. Published peer-reviewed studies were eligible if they enrolled participants with COPD who underwent PR with an exercise component ≥ 4 weeks and measured inflammatory biomarkers pre- and post-intervention. Outcomes of interest were TNF-α, CRP, and IL-6. We conducted meta-analyses and reported the mean difference (MD) and 95% confidence intervals. We screened 17,337 studies, with 5 RCTs and 12 observational trials meeting inclusion criteria, all with moderate to high risk of bias. A total of 1,277 participants were enrolled across all eligible studies. Meta-analyses of the 5 RCTs found that TNF-α (MD −0.53, 95% CI −0.66 to −0.40, I2 = 80%) and CRP (MD -1.19, 95% CI -1.78 to −0.60, I2 = 86%) had greater reductions with PR than usual care. No difference between groups was noted for IL-6 (MD -3.38, 95% CI -13.03 to 6.28, I2 = 98%). A PR program with ≥ 4 weeks of exercise appears to reduce TNF-α and CRP levels but not IL-6 in people with COPD. These results are based on small sample sizes. Further high quality RCTs are needed to confirm PR’s impact on systemic inflammation.
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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 | 0.015 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.045 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".