Renin–angiotensin–aldosterone system blockade for cardiovascular diseases: current status
Bibliographic record
Abstract
Activation of the renin-angiotensin-aldosterone system (RAAS) results in vasoconstriction, muscular (vascular and cardiac) hypertrophy and fibrosis. Established arterial stiffness and cardiac dysfunction are key factors contributing to subsequent cardiovascular and renal complications. Blockade of RAAS has been shown to be beneficial in patients with hypertension, acute myocardial infarction, chronic systolic heart failure, stroke and diabetic renal disease. An aggressive approach for more extensive RAAS blockade with combination of two commonly used RAAS blockers [ACE inhibitors (ACEIs) and angiotensin receptor blockers (ARBs)] yielded conflicting results in different patient populations. Combination therapy is also associated with more side effects, in particular hypotension, hyperkalaemia and renal impairment. Recently published ONTARGET study showed ACEI/ARB combination therapy was associated with more adverse effects without any increase in benefit. The Canadian Hypertension Education Program responded with a new warning: 'Do not use ACEI and ARB in combination'. However, the European Society of Cardiology in their updated heart failure treatment guidelines still recommended ACEI/ARB combo as a viable option. This apparent inconsistency among guidelines generates debate as to which approach of RAAS inhibition is the best. The current paper reviews the latest evidence of isolated ACEI or ARB use and their combination in cardiovascular diseases, and makes recommendations for their prescriptions in specific patient populations.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".