The impact of isolated inspiratory muscle training on exercise capacity, dyspnea, and quality of life in heart failure patients: a systematic review and meta-analysis
Bibliographic record
Abstract
Introduction Recently, inspiratory muscle training (IMT) has emerged as an alternative therapeutic approach for patients with heart failure (HF), characterized by ventilation inefficiency and reduced functional capacity. However, the evidence regarding the effects of IMT on patients with HF remains ambiguous. This meta-analysis aims to ascertain the impact of isolated IMT on the 6-minute walk test (6MWT), VO2peak, dyspnea, resting heart rate (RHR), and Quality of Life (QoL) in patients with HF.Methods A systematic literature search was conducted across four databases: Science Direct, PubMed, Cochrane, and EBSCO. Two reviewers independently performed literature searches and screenings. The quality and risk of bias were assessed using the Physiotherapy Evidence Database (PEDro) scale and the Cochrane Risk-of-Bias tool for randomized trials (RoB2). Meta-analysis was conducted using Revman 5.4.1 and Open Meta-analyst.Results Seven clinical trials involving 211 patients with HF were included. Meta-analysis revealed that IMT with intensity above 25% Maximum Inspiratory Pressure (MIP) significantly improved the 6MWT (61.81 m; 95% CI: 10.99 − 112.63 m), VO2peak (3.82 ml/kg/min; 95% CI: 3.25 − 4.39 ml/kg/min), dyspnea (−0.44; 95% CI: −0.76 to −0.12), and QoL (−12.23; 95% CI: −21.28 to −3.19), but not in RHR (−4.59; 95% CI: −10.91 to 1.72).Conclusion IMT with intensity above 25% MIP demonstrated improvements in the 6MWT, VO2peak, dyspnea, and QoL in patients with HF.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.044 |
| 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 | 0.003 | 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".