Usefulness of Carvedilol in the Treatment of Chronic Aortic Valve Regurgitation
Bibliographic record
Abstract
BACKGROUND: Aortic regurgitation (AR) is a chronic disease for which there is currently no approved medical treatment. We previously reported in an animal model that β-blockade with metoprolol exerted beneficial effects on left ventricular remodeling and survival. Despite the recent publication of promising human data, β-blockade in chronic AR remains controversial. More data are needed to support this potentially new treatment strategy. We hypothesized that carvedilol might be another safe treatment option in chronic AR, considering its combined β-blocking and α-blocking effects and proven efficacy in patients with established heart failure. METHODS AND RESULTS: The effects of a 6-month treatment with carvedilol 30 mg/kg/d orally were evaluated in adult Wistar rats with severe AR. Sham-operated and untreated AR animals were used as controls. Carvedilol treatment resulted in less left ventricular hypertrophy and dilatation. Ejection fraction was improved and filling pressures were reduced by carvedilol. β1-Receptor expression was also improved as well as myocardial capillary density. Those beneficial effects were noted despite the presence of drug-induced bradycardia. CONCLUSIONS: Carvedilol exerted protective effects against volume-overload cardiomyopathy in this model of aortic valve regurgitation with preserved ejection fraction. These results suggest a protective class effect of β-blockers. Combined with the recent publication of promising human data, our findings support the need to carefully design a prospective study in humans to evaluate the effects of β-blockers in chronic aortic valve regurgitation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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; 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".