Optimizing Foundational Therapies in Patients With HFrEF
Bibliographic record
Abstract
Given the high risk of adverse outcomes in patients with heart failure and reduced ejection fraction (HFrEF), there is an urgent need for the initiation and titration of guideline-directed medical therapy (GDMT) that can reduce the risk of morbidity and mortality. Clinical practice guidelines are now emphasizing the need for early and rapid initiation of therapies that have cardiovascular benefit. Recognizing that there are many barriers to GDMT initiation and optimization, health care providers should aim to introduce the 4 pillars of quadruple therapy now recommended by most clinical practice guidelines: angiotensin receptor-neprilysin inhibitors, beta-blockers, mineralocorticoid receptor antagonists, and sodium-glucose co-transporter 2 inhibitors. A large proportion of patients with HFrEF do not have clinical contraindications to GDMT but are not treated with these therapies. Early initiation of low-dose combination therapy should be tolerated by most patients. However, patient-related factors such as hemodynamics, frailty, and laboratory values will need consideration for maximum tolerated GDMT. GDMT initiation in acute heart failure hospitalization represents another important avenue to improve use of GDMT. Finally, removal of therapies that do not have clear cardiovascular benefit should be considered to lower polypharmacy and reduce the risk of adverse side effects. Future prospective studies aimed at guiding optimal implementation of quadruple therapy are warranted to reduce morbidity and mortality in patients with HFrEF.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".