Cardiac Shock Wave Therapy for Coronary Heart Disease: an Updated Meta-analysis
Bibliographic record
Abstract
INTRODUCTION: The aim of this article is to study the efficacy and safety of cardiac shock wave therapy (CSWT) in the treatment of coronary heart disease (CAD). METHODS: A comprehensive search of electronic databases and a manual search of conference papers and abstracts were performed until September 30, 2018. The studies using RevMan 5.3 and STATA 14.0 softwares were reviewed, and meta-analyses were performed on 13 indicators, such as a six-min walking distance test (6MWT), New York Heart Association (NYHA) functional class, Seattle Angina Questionnaire (SAQ) score, angina class (Canadian Cardiology Society [CCS]), etc. RESULTS: A total of 26 articles were included. The total patient population was 855, of which 781 patients were treated with CSWT. Meta-analyses indicated that 6MWT (mean difference [MD] 75.64, 95% confidence interval [CI] 49.03, 102.25, P<0.00001) and NYHA (MD -0.70, 95% CI -0.92) in the CSWT group were comparable to those in the conventional revascularization group (MD -0.70, 95% CI -0.92, -0.49, P<0.00001). SAQ (MD 10.75, 95% CI 6.66, 14.83, P<0.00001), CCS (MD -0.99, 95% CI -1.13, -0.84, P<0.00001), nitrate dosage (MD -1.84, 95% CI -2.77, -1.12, P<0.00001), LVEF (MD 3.77, 95% CI 2.17, 5.37, P<0.00001), and SSS (MD -4.29, 95% CI -5.61, -2.96, P<0.00001), SRS (MD -2.90, 95% CI -4.85, -0.95, P=0.004), and the exercise test (standard mean difference 0.57, 95% CI 0.12, 1.02, P=0.01) all showed significant differences. CONCLUSION: CSWT may offer beneficial effects to patients with CAD, but more large-scale clinical studies are needed to further verify its therapeutic effect.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.035 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".