Management of Aortic Stenosis and Chronic Heart Failure: A Clinical Consensus Statement of the Heart Failure Association (HFA) and the European Association of Percutaneous Cardiovascular Interventions (EAPCI) of the ESC
Bibliographic record
Abstract
Aortic stenosis (AS) is common and can cause heart failure (HF) or contribute to the progression of pre-existing HF. The management of patients with concomitant AS and HF poses specific clinical challenges. Optimization of guideline-directed medical therapy for HF may be difficult in patients with AS, especially in case of reduced left ventricular ejection fraction. Transcatheter or surgical aortic valve replacement (AVR) is the evidence-based treatment of choice for patients with severe AS and HF. However, advanced cardiac damage, concomitant conditions that can cause HF in addition to AS, as well as some procedure-related factors, may contribute to persistence or worsening of HF after AVR. A multidisciplinary management involving an HF specialist is crucial in this setting and should include a dedicated pre-procedural HF and AS assessment, as well as a careful post-procedural follow-up, including monitoring of HF status. The aim of this clinical consensus statement is to summarize current knowledge on AS and HF, with a focus on pre-procedural and post-procedural management of patients with HF undergoing AVR.
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.
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 | 0.018 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".