Clinical effect of cardiac shock wave therapy on patients with ischaemic heart disease: a systematic review and meta‐analysis
Bibliographic record
Abstract
BACKGROUND: After several years of study, CSWT has been initially applied to IHD treatment, but the actual effectiveness has never been well evaluated with a meta-analysis. METHODS: MEDLINE, EMBASE, Science Direct, Cochrane Controlled Trials Register database and Chinese database were searched. The randomized controlled trials, and single-arm and cohort study related to in patients with IHD undergoing CSWT were included and 14 articles were finally analysed. The data related to the study design, patient characteristics and outcomes were extracted. All the selected data were calculated with random-effects models in weighted mean differences, and heterogeneity was carefully evaluated as well. RESULTS: (i) Cardiac shock wave therapy improves the angina pectoris symptom (including the decrease of Canadian Cardiovascular Society class [-0·86 (-1·12, -0·65), P < 0·00001], nitroglycerin dosage (times/weeks) [-0·71 (-1·08, -0·33), P = 0·0002] and a increase of Seattle Angina Questionnaire score [5·64 (3·12, 8·15), P < 0·0001)]); (ii) CSWT leads to a reduce in heart failure (including a reduction of New York Heart Association functional class [-0·49 (-0·62, -0·37), P < 0·00001], a stable rise in 6-min walking distance [68·38 (39·70, 97·05), P < 0·00001] and a growth in left ventricular ejection fraction with echocardiography screening [6·73 (4·67,8·80), P < 0·00001]); (iii) CSWT improves myocardial viability within improving in total score of perfusion imaging [-5·19 (-8·08, -2·30), P = 0·0004] and total score of metabolism imaging [-5·33 (-7·77, -2·90), P < 0·0001]. CONCLUSIONS: The meta-analysis suggests that CSWT may offer beneficial effects to patients with IHD, although there was significant heterogeneity across the studies.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.031 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".