Should treatment for heart failure with preserved ejection fraction differ from that for heart failure with reduced ejection fraction?
Bibliographic record
Abstract
Heart failure with preserved ejection fraction (HF-PEF) accounts for about half of all cases of heart failure.1 Population based studies have shown that patients with HF-PEF have high rates of mortality (20-30% risk at one year)2 3 and readmission to hospital (30% risk at 60-90 days),4 which are similar to the rates for patients with heart failure with reduced ejection fraction (HF-REF). When compared with patients with HF-REF, those with HF-PEF are older, more often female, and more likely to have hypertension and atrial fibrillation, but are less likely to have coronary artery disease.2 3 Identifying patients with HF-PEF can be difficult, however, because heart failure presents in many different ways and the diagnostic criteria that define this syndrome are complex.5 The guideline from National Institute for Health and Clinical Excellence has proposed detailed guidance on diagnosis.6 In practice, the diagnosis of HF-PEF is often made in patients with symptoms and signs of heart failure who have preserved left ventricular ejection fraction. Furthermore, the evidence that supports treatment for HF-PEF is much weaker than that supporting treatment for HF-REF. To date, results from existing clinical trials of HF-PEF have been largely inconclusive, and treatments that have been shown to reduce morbidity and mortality in patients with HF-REF showed either no or only marginal benefits in patients with HF-PEF. To clarify whether treatments that are beneficial in HF-REF may also be beneficial in HF-PEF, we searched Medline, Embase, and the Cochrane Library for all adequately powered randomised controlled trials of treatments for chronic heart failure (HF-PEF or HF-REF) that had reported both mortality and admission to hospital as their primary outcomes. We found no meta-analyses of treatments for HF-PEF, so we depended on studies that would provide the most robust and unbiased estimates of the …
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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 | 0.009 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.013 | 0.014 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".