Update on diastolic heart failure or heart failure with preserved ejection fraction in the older adults
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it