Secondary valve regurgitation in patients with heart failure with preserved ejection fraction, heart failure with mid-range ejection fraction, and heart failure with reduced ejection fraction
Bibliographic record
Abstract
Secondary mitral regurgitation and secondary tricuspid regurgitation due to heart failure (HF) remain challenging in almost every aspect: increasing prevalence, poor prognosis, notoriously elusive in diagnosis, and complexity of therapeutic management. Recently, defined HF subgroups according to three ejection fraction (EF) ranges (reduced, mid-range, and preserved) have stimulated a structured understanding of the HF syndrome but the role of secondary valve regurgitation (SVR) across the spectrum of EF remains undefined. This review expands this structured understanding by consolidating the underlying phenotype of myocardial impairment with each type of SVR. Specifically, the current understanding, epidemiological considerations, impact, public health burden, mechanisms, and treatment options of SVR are discussed separately for each lesion across the HF spectrum. Furthermore, this review identifies important gaps in knowledge, future directions for research, and provides potential solutions for diagnosis and treatment. Mastering the challenge of SVR requires a multidisciplinary collaborative effort, both, in clinical practice and scientific approach to optimize patient outcomes.
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 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 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".