Effectiveness of Repeat Enhanced External Counterpulsation for Refractory Angina in Patients Failing to Complete an Initial Course of Therapy
Bibliographic record
Abstract
AIMS: This study examined the causes and results of retreatment of patients who failed to complete an initial 35-hour Enhanced External Counterpulsation (EECP) course. METHODS AND RESULTS: Data of 2,311 successive angina patients from the International EECP Patient Registry were analyzed, 86.5% completed their EECP course (Complete cohort). Of the 13.5% patients failing to complete the initial course (Incomplete cohort), 28.3% had repeat EECP within 1 year vs. 10.1% of the Complete group. The predictors of failure to complete the initial course of EECP were: female gender, heart failure, use of angiotensin-converting enzyme inhibitors or angiotensin receptor blockers, and use of nitroglycerin. For the Complete group, 83.4% had a reduction of at least one Canadian Cardiovascular Society (CCS) class after their initial EECP course, vs. 21.7% in the Incomplete group (p < 0.001). After repeat treatment, 66.2% of the Incomplete group achieved at least one CCS class reduction vs. 69.4% of the Complete group (p = NS) undergoing retreatment. The independent predictors for those who return to successfully complete their second course were patients who stopped their first course because of clinical events, and candidacy for coronary artery bypass grafting at the time of initial treatment. CONCLUSION: The results of retreatment of those who failed to complete their initial EECP course were comparable to those who completed their initial treatment, with similar reductions of CCS angina class.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".