The efficacy of inspiratory muscle training in patients with coronary artery disease: Protocol for a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Inspiratory muscle training (IMT) has been recognized as an effective form of training in patients with cardiovascular disease and heart failure. However, little is known about the efficacy of IMT in the treatment of patients with coronary artery disease (CAD). The aim of this systematic review will be to evaluate randomized controlled studies to understand the effect of IMT on CAD patients. METHOD: We will include randomized controlled trials evaluating the efficacy of IMT in patients 18 years and older diagnosed with CAD. Crossover trials, cluster-randomized, quasi-randomized, and non-randomized trials will be excluded. Study search will be conducted in major databases (MEDLINE, the Cochrane Central Register of Controlled Trials, Embase, and PEDro). The study intervention will be IMT independent of the duration, frequency, or intensity of training. The primary outcome will be quality of life, patient-reported health status, and all adverse events related to IMT. Secondary outcomes will include exercise capacity and respiratory muscle strength. The risk of bias will be evaluated based on the Cochrane Risk of Bias tool. Screening, data extraction, and quality assessment will be performed by two independent reviewers. If two or more studies are considered to be clinically homogeneous, a meta-analysis based on the random-effects model will be performed. The quality of evidence will be evaluated based on the GRADE approach. CONCLUSION: This systematic review will improve our understanding of the effects of IMT on CAD patients and potentially establish IMT as an alternative form of exercise training for the treatment of CAD. TRIAL REGISTRATION: Study registration. OSF registries (https://osf.io/3ch7m). Date registered: May 10, 2022. Registration DOI: https://doi.org/10.17605/OSF.IO/GVMY7.
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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 | 0.056 | 0.078 |
| Meta-epidemiology (narrow) | 0.007 | 0.005 |
| Meta-epidemiology (broad) | 0.025 | 0.030 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.051 | 0.005 |
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".