Cardiac schockwave therapy in the treatment of ischemic heart disease patiens with refractive angina pectoris
Bibliographic record
Abstract
Objective: To evaluate of the effectiveness of cardiac shockwave therapy in the treatment of ischemic heart disease with refractive angina pectoris. Subject and method: A prospective, cross-sectional study with comparison and follow-up for 6 months on 50 patients with refractive angina pectoris from January 2017 to January 2020. The protocol application of 100 shocks/spot at 0.09mJ/mm2 energy flux density for 3 - 6 spots each time, with three times per week at each series for three series at 1, 5, 9 weeks. Result: The average age was 71.32 ± 10.5 years, men accounted for 78%. The symptoms of angina improved significantly (amount of chest pain 5.87 ± 2.7 to 0.28 ± 0.45 times; Nitrat consumed per week from 6.3 ± 3.5 to 0.3 ± 0.5 tablets/week). The 6-minute walking test all improved (278.1 ± 71m compared with 390.5 ± 42.3m). CCS angina class improved significant. NYHA grade improved significantly (NYHA III from 40.7% to 11.1%, NYHA II from 51.9% to 33.3%). Pro-BNP decreased (994.99 ± 1708.9 to 429.0 ± 453.9 pg/ml). WSMI decreased from 1.49 ± 0.22 to 1.24 ± 0.12, GLS improved from -9.79 ± 2.68 to -12.7 ± 2.42. Average score of SSS, SRS, SDS markedly improved with p<0.05 by SPECT. The degree of severe perfusion defect and the wide perfusion defect area decreased significantly after treatment by 52% to 12% and 58% to 28%, respectively. Conclusion: Cardiac shockwave therapy improved clinical symptoms and increased myocardial perfusion in ischemic heart disease with refractive angina pectoris.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".