Therapeutic Controversies in the Medical Management of Valvular Heart Disease
Bibliographic record
Abstract
Objective: To evaluate the evidence for common therapeutic controversies in the medical management of valvular heart disease (VHD). Data Sources: A literature search of PubMed (inception to December 2020) was performed using the terms angiotensin-converting enzyme (ACE) inhibitors or angiotensin receptor blockers (ARBs) and aortic stenosis (AS); and adrenergic β-antagonists and aortic valve regurgitation (AR) or mitral stenosis (MS). Study Selection and Data Extraction: Randomized controlled trials (RCTs) and meta-analyses conducted in humans and published in English that reported ≥1 clinical outcome were included. Data Synthesis: Nine articles were included: 3 RCTs and 1 meta-analysis for ACE inhibitors/ARBs in AS, 1 RCT for β-blockers in AR, and 4 RCTs for β-blockers in MS. Evidence suggests that ACE inhibitors/ARBs do not increase the risk of adverse outcomes in patients with AS but may delay valve replacement. β-Blockers do not appear to worsen outcomes in patients with chronic AR and may improve left-ventricular function in patients with a reduced ejection fraction. β-Blockers do not improve and may actually worsen exercise tolerance in patients with MS in sinus rhythm. Relevance to Patient Care and Clinical Practice: ACE inhibitors/ARBs and β-blockers can likely be safely used in patients with AS or AR, respectively, who have a compelling indication. There is insufficient evidence to recommend routine use of β-blockers in patients with MS without atrial fibrillation. Conclusions: Common beliefs about the medical treatment of VHD are not supported by high-quality data. There remains a need for larger-scale RCTs in the medical management of VHD.
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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 | 0.099 | 0.311 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".