VALsartan In Acute Myocardial Infarction (VALIANT) Trial: Baseline Characteristics in Context
Bibliographic record
Abstract
BACKGROUND: The VALsartan In Acute myocardial iNfarcTion (VALIANT) trial compared outcomes with: (1) angiotensin-converting enzyme inhibition (ACEI) with the reference agent captopril; (2) angiotensin-receptor blockade (ARB) with valsartan; or (3) both in patients with heart failure (HF) and/or left ventricular systolic dysfunction (LVSD) after myocardial infarction (MI). AIMS: a goal of this active-control trial was to simulate conditions that would lead current practitioners to use ACEIs. Thus, we compared characteristics of VALIANT patients with those of patients in placebo-controlled trials that established ACEIs as standard treatment. METHODS AND RESULTS: We collected demographic, clinical, medication and imaging information from 14703 patients in 24 countries. This high-risk population was a median 65.8 years old, and 31.1% were female. Most (51.8%) showed imaging evidence of LVSD at enrollment. Most (72%) had Killip class>/=II HF. Patients received evidence-based therapies at rates similar to those of contemporary MI trials and at an improved rate compared with prior placebo-controlled ACEI trials. CONCLUSION: VALIANT represents the largest globally representative cohort enrolled with HF and/or LVSD after MI. Patients were similar to those in placebo-controlled ACEI trials while reflecting improvements in evidence-based care. With enrollment complete, VALIANT is poised to define the optimal strategy for renin-angiotensin system blockade after MI to improve cardiovascular outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".