Bibliographic record
Abstract
Angiotensin receptor blockers (ARBs) were introduced after clinical trials showed angiotensin-converting enzyme inhibitors (ACEIs) to have extensive clinical benefits in a wide range of diseases. Consequently, it has been more difficult for clinical trials to demonstrate similar, enhanced or additive benefits of ARBs. However, ARBs were introduced with the hypothesis that they were likely a more effective method of interrupting the renin-angiotensin system and would result in enhanced outcomes. Clinical trials in high-risk vascular patients (after myocardial infarction), patients with heart failure and patients with nephropathy show the benefits of ACE inhibition. ARBs likely have similar benefits as ACEIs when used after myocardial infarction, in patients with heart failure and for management of diabetic nephropathy. However, ARBs generally remain a second-line treatment because it has been more difficult to demonstrate that ARBs prevent acute vascular events, such as myocardial infarction, together with the greater clinical trial evidence for ACE inhibition. The primary application of ACEIs over ARBs is reflected in the Canadian clinical guidelines for the management of patients with diabetes, hypertension, heart failure and following myocardial infarction. Until the completion of clinical trials, such as the Ongoing Telmisartan Alone and in Combination with Ramipril Global Endpoint Trial (ONTARGET), that examine whether ARBs have vascular protective properties similar to ACEIs, it is unlikely that the clinical guidelines will change.
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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 | 0.024 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.005 | 0.013 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.010 | 0.021 |
| Insufficient payload (model declined to judge) | 0.016 | 0.006 |
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".