Inspiratory muscle training, with or without concomitant pulmonary rehabilitation, for COPD: state of the evidence from a Cochrane review
Bibliographic record
Abstract
Background: Inspiratory muscle training (IMT) is a rehabilitation technique aiming to improve inspiratory muscle strength and endurance. Aim: This Cochrane systematic review (published on 06 January 2023) aims to explore the effect of IMT as a stand-alone intervention and when combined with pulmonary rehabilitation (PR) for patients with COPD. Methods: We conducted a comprehensive literature search (last update 20 October 2022). We included RCTs that compared (PR+IMT vs PR) and (IMT vs control/sham) except those that used resistive devices without controlling the breathing pattern or a training load less than 30% of PImax. We used the new Cochrane tool of risk of bias assessment (ROB 2.0) and the five domains of GRADE to assess the certainty of the evidence. Results: 22 RCTs (1446 participants) compared (PR+IMT vs PR). We did not find an improvement in dyspnea (Borg scale, moderate certainty of evidence), exercise capacity (6MWD, very low certainty of evidence) and quality of life (SGRQ, low certainty of evidence). 37 RCTs (1021 participants) compared (IMT vs control/sham). There was a trend towards improving Borg and the SGRQ (very low certainty of evidence) and a better effect with the 6MWD (moderate certainty of evidence). For both comparisons, we did not find a larger effect with longer training durations and in participants with respiratory muscle weakness. Conclusion: Future research should focus on participants with respiratory muscle weakness, explore whether IMT may be a start intervention for patients unable to undergo PR, compare different training protocols and consider training with higher inspiratory flow rate and at high lung volume.
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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 | 0.007 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.012 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".