The effects of pulmonary rehabilitation on inflammatory biomarkers in patients with chronic obstructive pulmonary disease: A systematic review and meta-analysis
Bibliographic record
Abstract
INTRODUCTION Chronic obstructive pulmonary disease (COPD) is associated with elevated levels of proinflammatory biomarkers which may contribute to pathophysiological changes in both the respiratory and extrapulmonary systems. Pulmonary rehabilitation (PR) is an effective intervention for symptom management in people with COPD. The impact of PR on systemic inflammation is currently unknown.MATERIALS AND METHODS Using the search terms “chronic obstructive pulmonary disease,” “pulmonary rehabilitation” and “inflammatory biomarkers,” 5 databases were searched from inception. Studies were eligible if they included: (1) participants with COPD undergoing PR with an exercise component of at least 4 weeks in length and (2) a systemic inflammation measurement pre- and post-PR. Our primary outcomes of interest were interleukin-6 (IL-6), fibrinogen and c-reactive protein (CRP) concentrations. A meta-analysis was performed using weighted mean difference and 95% confidence intervals.RESULTS A total of 1339 participants were enrolled across all 18 eligible studies. A meta-analysis of the 5 randomized controlled trials (RCTs) found that CRP (MD −1.19, 95% CI −1.78 to −0.60, I2 = 86%) showed a greater response to 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%). Fibrinogen was not reported among included RCTs.CONCLUSION A PR program with at least 4 weeks of exercise appears to reduce CRP levels but not IL-6 or fibrinogen in people with COPD. These results are based on small sample sizes and low-quality evidence and the impact of PR on systemic inflammation is uncertain.
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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 | 0.013 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.042 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".