Update on diastolic heart failure or heart failure with preserved ejection fraction in the older adults
Bibliographic record
Abstract
Nearly half of all heart failure (HF) patients have diastolic HF (DHF) or clinical HF with normal or near-normal left ventricular ejection fraction (LVEF). Although the terminology has not been clearly defined, it is increasingly being referred to as HF with preserved ejection fraction (HFPEF). The prevalence of HFPEF increases with age, especially among older women. Identifying HFPEF is important because the etiology, pathogenesis, prognosis, and optimal management may differ from that for systolic HF (SHF) or HF with reduced ejection fraction. The clinical presentation of HF is similar for both SHF and HFPEF. As in SHF, HFPEF is a clinical diagnosis. Once a clinical diagnosis of HF has been made, the presence of HFPEF can be established by confirming a normal or near-normal LVEF, often by an echocardiogram. HFPEF is often associated with a history of hypertension, concentric left ventricular hypertrophy, vascular stiffness, and left ventricular diastolic dysfunction. As in SHF, HFPEF is also associated with poor outcomes. While therapies with angiotensin-converting enzyme inhibitors and beta-blockers improve outcomes in SHF, there is currently no such evidence of their benefits in older HFPEF patients. In this review recent advances in the diagnosis and management of HFPEF in older adults are discussed.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".