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Record W3183863104 · doi:10.3389/fcvm.2021.664433

Cardiac Shock Wave Therapy Ameliorates Myocardial Ischemia in Patients With Chronic Refractory Angina Pectoris: A Randomized Trial

2021· article· en· W3183863104 on OpenAlexaboutno aff
Fan Ximin, Jianying Shen, Yawei Xu, Hong Liqiong

Bibliographic record

VenueFrontiers in Cardiovascular Medicine · 2021
Typearticle
Languageen
FieldMedicine
TopicPain Management and Treatment
Canadian institutionsnot available
FundersShanghai Shen Kang Hospital Development Center
KeywordsMedicineAnginaCanadian Cardiovascular SocietyCardiologyInternal medicineAdverse effectPerfusionRefractory (planetary science)Clinical trialRandomized controlled trialMyocardial infarction

Abstract

fetched live from OpenAlex

Background: Cardiac shock wave therapy (CSWT) is a non-invasive new option for the treatment of chronic refractory angina pectoris (CRAP). This study aimed to evaluate the safety and efficiency of CSWT in the treatment of CRAP. Methods: Eighty-seven patients with CRAP were randomly allocated into CWST group ( n = 46) and Control group ( n = 41). Canadian Cardiovascular Society (CCS) grade of angina pectoris, Seattle Angina Questionnaire (SAQ) score, 6-min walk test (6MWT), weekly dosage of nitroglycerin, and myocardial perfusion on D-SPECT were determined at baseline and during the follow-up period. Adverse events were also evaluated. Results: CSWT was well-tolerated in the CSWT patients. CSWT significantly improved the CCS grade, SAQ score, and 6MWT ( p < 0.05). Imaging examinations showed that the ischemic area was reduced after CSWT. However, no significant changes were observed in the Control group. Conclusions: CSWT may improve the myocardial perfusion and reduce clinical symptoms without increasing adverse effects in CRAP patients. It provides a non-invasive and safe clinical therapy for CRAP patients. Clinical Trial registration: www.ClinicalTrials.gov , identifier: NCT03398096.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.308
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.008
GPT teacher head0.220
Teacher spread0.212 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designRandomized trial
Domainnot available
GenreEmpirical

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".

Quick stats

Citations11
Published2021
Admission routes1
Has abstractyes

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