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
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.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".