Impact of diabetes on outcomes in patients with low and preserved ejection fraction heart failure: An analysis of the Candesartan in Heart failure: Assessment of Reduction in Mortality and morbidity (CHARM) programme
Bibliographic record
Abstract
AIMS: To determine whether the risk of adverse cardiovascular (CV) outcomes associated with diabetes differs in patients with low and preserved ejection fraction (EF) heart failure (HF). METHODS AND RESULTS: We analysed outcomes in the Candesartan in Heart failure-Assessment of Reduction in Mortality and morbidity (CHARM) programme which randomized 7599 patients with symptomatic HF and a broad range of EF. The prevalence of diabetes was 28.3% in patients with preserved EF (>40%) and 28.5% in those with low EF (<or=40%). Diabetes was associated with a greater relative risk of CV death or HF hospitalization in patients with preserved EF [hazard ratio (HR) 2.0 (1.70-2.36)] than in patients with low EF [HR 1.60 (1.44-1.77); interaction test P = 0.0009]. For all-cause mortality, the risk conferred by diabetes was similar in both low and preserved EF groups. The effect of candesartan in reducing CV morbidity and mortality outcomes was not modified by having diabetes at baseline (P = 0.09 test for interaction). CONCLUSION: Diabetes was an independent predictor of CV morbidity and mortality in patients with HF, regardless of EF. The relative risk of CV death or HF hospitalization conferred by diabetes was significantly greater in patients with preserved when compared with those with low EF HF.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".