Impact of Autonomic Regulation Therapy in Patients with Heart Failure
Bibliographic record
Abstract
BACKGROUND: The ANTHEM-HFrEF (Autonomic Regulation Therapy to Enhance Myocardial Function and Reduce Progression of Heart Failure with Reduced Ejection Fraction) pivotal study is an adaptive, open-label, randomized, controlled study evaluating whether autonomic regulation therapy will benefit patients with advanced HFrEF. While early-phase studies have supported potential use of vagus nerve stimulation to deliver autonomic regulation therapy for HFrEF, results of larger clinical trials have been inconsistent. The ANTHEM-HFrEF study uses a novel design, with adaptive sample size selection, evaluating effects on morbidity and mortality as well as symptoms and function. METHODS: The ANTHEM-HFrEF study will randomize patients (2:1) to autonomic regulation therapy plus guideline-directed medical therapy, or guideline-directed medical therapy alone. The morbidity and mortality trial utilizes a conventional frequentist approach for analysis of the primary outcome end point-reduction in the composite of cardiovascular death or first HF hospitalization-and a Bayesian adaptive approach toward sample size selection. Embedded within the ANTHEM-HFrEF study is a second trial evaluating improvement in symptoms and function. Symptom/function success will require meeting 2 risk-related conditions (trend for reduced cardiovascular death/HF hospitalization and sufficient freedom from device-related serious adverse events) and 3 efficacy end point components (changes in left ventricular EF, 6-minute walk distance, and Kansas City Cardiomyopathy Questionnaire overall score). CONCLUSIONS: Vagus nerve stimulation remains a promising, yet unproven treatment in HFrEF. A successful ANTHEM-HFrEF pivotal study would provide an important advance in HFrEF treatment and offer a model for expediting evaluation of new therapies. CLINICAL TRIAL REGISTRATION: URL: http://www.clinicaltrials.gov. Unique identifier: NCT03425422.
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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 | 0.002 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".