Initial data supporting the design of the Candesartan in Heart failure — Assessment of Reduction in Mortality and morbidity (CHARM) programme
Bibliographic record
Abstract
BACKGROUND: The therapies developed to treat heart failure over the years have resulted in a significant improvement in clinical outcome. The 1-year mortality following hospital discharge remains unacceptably high, however. Furthermore, a significant number of patients are unable to tolerate angiotensin-converting enzyme (ACE) inhibitors. Clearly, scope remains for the improvement of neurohormonal blockade in patients with heart failure, and there is a particular need for alternative therapies in patients who are unable to tolerate ACE inhibitors. The use of angiotensin II receptor blockers may provide a means of fulfilling these needs. OBJECTIVES: This paper reviews the studies examining the angiotensin II receptor blocker candesartan in comparison with placebo, in comparison with ACE inhibitors, and in combination with ACE inhibitors. CONCLUSIONS: Overall the review found candesartan was effective and safe in various clinical settings. These initial data were used to design the Candesartan in Heart failure--Assessment of Reduction in Mortality and morbidity (CHARM) programme. The mechanistic studies performed prior to the CHARM programme supported the rationale to design a large trial examining the effects of candesartan on clinical events.
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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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".