Effect of Candesartan on Cause-Specific Mortality in Heart Failure Patients
Bibliographic record
Abstract
BACKGROUND: Patients with heart failure are at increased risk of sudden death and death attributed to progressive pump failure. We assessed the effect of candesartan on cause-specific mortality in patients enrolled in the Candesartan in Heart failure Assessment of Reduction in Mortality and morbidity (CHARM) program. METHODS AND RESULTS: The CHARM program consisted of 3 component trials that enrolled patients with symptomatic heart failure: CHARM-Alternative (n=2028; LVEF<=40% [corrected] and ACE intolerant), CHARM-Added (n=2548; LVEF<=40%, [corrected] already on ACE inhibitors), and CHARM-Preserved (n=3023; LVEF >40%). Patients were randomized to candesartan, titrated to 32 mg QD, or placebo and were followed up for a median of 37.7 months. All deaths were reviewed by a blinded adjudication committee and categorized according to prespecified definitions on the basis of a narrative and source documentation. The number and rate of deaths by cause were calculated for each of the component trials and the overall program. Of all the patients, 8.5% died suddenly, and 6.2% died of progressive heart failure. Candesartan reduced both sudden death (HR 0.85 [0.73 to 0.99], P=0.036) and death from worsening heart failure (HR 0.78 [0.65 to 0.94], P=0.008). These reductions were most apparent in the patients with LVEF<=40% [corrected]. CONCLUSIONS: Candesartan reduced sudden death and death from worsening heart failure in patients with symptomatic heart failure, although this reduction was most apparent in patients with systolic dysfunction.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.001 | 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 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".