Predictors of treatment benefits after enhanced external counterpulsation in patients with refractory angina pectoris
Bibliographic record
Abstract
BACKGROUND: Enhanced external counterpulsation (EECP) is a noninvasive treatment that can decrease limiting symptoms in patients with refractory angina pectoris (RAP). Identifying responders to EECP is important as EECP is not widely available and relatively time intensive. HYPOTHESIS: The effect of EECP treatment on physical capacity in patients with RAP can be predicted from baseline patient characteristics and clinical factors. METHODS: This explorative study includes all patients from a cardiology clinic who had finished one EECP treatment and a 6 min walk test pre and post EECP. Clinical data, including Canadian Cardiovascular Society (CCS) classification and left ventricular ejection fraction (LVEF), were assessed before treatment. If patients increased their 6 min walking distance (6MWD) by 10% post EECP, they were considered responders. RESULTS: Of the 119 patients (men = 97, 40-91 years), 49 (41.2%) were responders. Multinomial regression analysis showed that functional status (i.e., CCS class ≥3) (OR 3.10, 95% CI 1.12-8.57), LVEF <50% (OR 2.82, 95% CI 1.02-7.80), and prior performed revascularization (i.e., ≤ 1 type of intervention) (OR 2.77, 95% CI 1.06-7.20) were predictors of response to EECP (p < .05, Accuracy 63.6%). Traditional risk factors (e.g., gender, smoking, and comorbidities) did not predict response. CONCLUSIONS: EECP treatment should be considered preferentially for patients that have a greater functional impairment, evidence of systolic left ventricular dysfunction, and exposure to fewer types of revascularization, either PCI or CABG. Improvement in 6MWD post EECP could imply improvement in physical capacity, which is a likely contributor to improved well-being among patients with RAP.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".