Patient Phenotype Profiling in Heart Failure with Preserved Ejection Fraction to Guide Therapeutic Decision Making. A Scientific Statement of the Heart Failure Association, the European Heart Rhythm Association of the European Society of Cardiology, and the European Society of Hypertension
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
Heart failure with preserved ejection fraction (HFpEF) represents a highly heterogeneous clinical syndrome affected in its development and progression by many comorbidities. The left ventricular diastolic dysfunction may be a manifestation of various combinations of cardiovascular, metabolic, pulmonary, renal, and geriatric conditions. Thus, in addition to treatment with sodium-glucose cotransporter 2 inhibitors in all patients, the most effective method of improving clinical outcomes may be therapy tailored to each patient's clinical profile. To better outline a phenotype-based approach for the treatment of HFpEF, in this joint position paper, the Heart Failure Association of the European Society of Cardiology, the European Heart Rhythm Association and the European Hypertension Society, have developed an algorithm to identify the most common HFpEF phenotypes and identify the evidence-based treatment strategy for each, while taking into account the complexities of multiple comorbidities and polypharmacy.
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 | 0.018 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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