Aiming at harmony. Comparing and contrasting International HFrEF Guidelines
Bibliographic record
Abstract
Large randomized controlled trials (RCTs) have led to major changes in the treatment of patients with heart failure and reduced left ventricular ejection fraction (HFrEF) and these advances are included in the recent European Society of Cardiology (ESC) and the American College of Cardiology/American Heart Association/Heart Failure Society of America (ACC/AHA/HFSA) guidelines issued in 2021 and 2022, respectively. According to both guidelines, treatment of patients with HFrEF is based on the administration of four classes of drugs that reduce the primary endpoint of cardiovascular death and HF hospitalizations in RCTs: angiotensin-converting enzyme or angiotensin receptor neprilysin inhibitors, beta-blockers, mineralocorticoid receptor antagonists, and sodium-glucose co-transporter 2 inhibitors. Specific sequences of treatment are not recommended but emphasis is given to reaching treatment with all four drugs as early as possible. Further treatments are considered in selected patients including ivabradine, hydralazine nitrates, digoxin, and the new agent vericiguat. Specific treatments, mostly new, for cardiovascular and non-cardiovascular comorbidities are also given. The aim of this article is to compare the two recent guidelines issued by the ESC and ACC/AHA/HFSA and show the few differences and the many consistent recommendations, now more numerous given the evidence available for many new treatments.
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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 | 0.232 | 0.444 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".