Clinical trials, the renin angiotensin system and atrial fibrillation
Bibliographic record
Abstract
PURPOSE OF REVIEW: Atrial fibrillation is the most common clinical arrhythmia. Current treatment strategies are far from optimal. One new research direction is to target the atrial fibrillation substrate and to examine whether drugs can produce atrial structural and/or electrophysiological remodeling and whether this results in a reduction in atrial fibrillation burden. RECENT FINDINGS: Two prospective randomized studies have shown that the addition of an angiotensin converting enzyme inhibitor or an angiotensin receptor blocker to amiodarone reduces the recurrence rate of atrial fibrillation after electrical cardioversion. There are ten completed prospective clinical trials with atrial fibrillation as a secondary endpoint or assessed in post-hoc analysis. Five of these studies have reported a positive impact of angiotensin converting enzyme inhibitors or angiotensin receptor blockers on atrial fibrillation burden. A meta-analysis showed that active drugs reduced the overall risk of development of atrial fibrillation by 28%. Patients in the heart failure trials obtained most benefit from these drugs (relative risk reduction 44%, P = 0.07). SUMMARY: The initial basic science and clinical trial data suggest that modulation of the renin angiotensin system may be an effective treatment for atrial fibrillation. The following, however, remain to be clarified: do these drugs have a clinically meaningful impact on atrial fibrillation burden; if there is an impact, is it similar in all atrial fibrillation patients or just in certain subsets; do angiotensin converting enzyme inhibitors and angiotensin receptor blockers have similar benefits; and is there a role for aldosterone antagonists?
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".