Impact of External Counterpulsation Treatment on Emergency Department Visits and Hospitalizations in Refractory Angina Patients With Left Ventricular Dysfunction
Bibliographic record
Abstract
Patients with refractory angina and left ventricular (LV) dysfunction exert an enormous burden on health care resources primarily because of the number of recurrent emergency department (ED) visits and hospitalizations. Enhanced external counterpulsation (EECP) therapy has emerged as a treatment option for patients with angina and LV dysfunction and has been shown to improve clinical outcomes and LV function. Improvements in symptoms and laboratory assessments in these patients, however, do not necessarily correlate with a reduction in ED visits and hospitalizations. This is the first study to assess the impact of EECP therapy on ED visits and hospitalization rates at 6‐month follow‐up. This prospective cohort study included 450 patients with LV dysfunction (ejection fraction ≤40%) treated with EECP therapy for refractory angina. Clinical outcomes, number of all‐cause ED visits, and hospitalizations within the 6 months before EECP therapy were compared with those at 6‐month follow‐up. Despite the unfavorable risk profile, refractory angina patients with LV dysfunction achieved a substantial reduction in all‐cause ED visits and hospitalization rates at 6‐month follow‐up. EECP therapy appears to offer an effective adjunctive treatment option for this group of patients.
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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 | 0.003 |
| 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 | 0.000 |
| 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".