Clinical effects of the enhanced external counterpulsation therapy in patients with ischemic chronic heart failure exacerbation COVID-19
Bibliographic record
Abstract
Abstract Objectives The aim of the study was to assess the effects of the enhanced external counterpulsation (EECP) therapy as a rehabilitation method in patients with ischemic chronic heart failure (CHF) after COVID-19. Methods 54 (n=54) stable symptomatic CHF (NYHA, functional class I-II; 35%≤LVEF≤50%) subjects (44 male and 10 female; mean age 61±9,8) with prior anamnesis of CAD, at least one myocardial infarction got the exacerbation of CHF after COVID-19 episode. They were randomized in a 2:1 manner into either 35 1-hour 250–300 mm Hg sessions of EECP (n=36; 30 male, 6 female) or Sham-EECP (n=18; 14 male, 4 female). All subjects had been received optimal CHF and CAD drug therapy. At baseline, a month and half a year after EECP course every subject was examined with echocardiography and 6-minute walk test. Results All 36 active EECP treatment group subjects improved by at least 1 NYHA class, 66% of them had no heart failure symptoms post treatment (p<0.01). 84% of treatment group pts. had sustained NYHA class improvement at half a year follow-up (p<0.01), compared with baseline. There was significant difference between LVEF 44±6,5% at baseline vs post-EECP LVEF 50±4,6% (p<0.01) in active EECP treatment group subjects. At the same time there were no significant changes of NYHA class and LVEF in Sham-EECP subjects. No one subject dies after half a year of follow up. Conclusions Enhanced external counterpulsation (EECP) therapy sustainably improves NYHA functional class and LVEF in patients with ischemic CHF exacerbation after COVID-19. Funding Acknowledgement Type of funding sources: None.
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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) | 0.001 | 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".