Abstract FR425: Antihypertensive Efficacy of Angiotensin Receptor Blockers with and without Inverse Agonism: A Systematic Review and Network Meta-analysis
Bibliographic record
Abstract
Objectives: Angiotensin receptor blockers (ARBs) with inverse agonism (IA) may have potential for more blood pressure (BP) reduction as compared to ARBs without IA, while the efficacy is yet to be elucidated. Methods: A systematic review and network meta-analysis identified randomized controlled trials (RCTs) of monotherapy for mild to moderate hypertension: azilsartan medoxomil, candesartan, olmesartan, losartan, valsartan, telmisartan, irbesartan, allisartan isoproxil, sacubitril/valsartan, sacubitril/allisartan, or placebo. Treatments were grouped as ARBs with or without IA, angiotensin receptor neprilysin inhibitors (ARNIs), and placebo. Azilsartan medoxomil was separately analyzed as part of ARBs with IA. BP changes was assessed. Results: Of 2,659 RCTs screened, 23 studies were analyzed. ARBs with IA demonstrated superior systolic BP and diastolic BP reductions compared to ARBs without IA, ARNIs, and placebo. When analyzed separately, azilsartan medoxomil significantly outperformed ARBs without IA in systolic BP and diastolic BP reduction, and was superior to other ARBs with IA in systolic BP reduction. Surface Under the Cumulative Ranking curves (SUCRA) indicated azilsartan medoxomil had the highest probability of being the best treatment for systolic BP (98%) and diastolic BP (95%) reduction. Conclusion: This study demonstrates that ARBs with IA, especially azilsartan medoxomil, have advantage on BP reduction as compared to ARBs without IA and ARNIs. Further study to explore the effects on long-term outcomes is warranted.
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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 | 0.012 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.035 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".