The effects of canagliflozin on heart failure and cardiovascular death by baseline participant characteristics: Analysis of the <scp>CREDENCE</scp> trial
Bibliographic record
Abstract
Abstract Heart failure is prevalent in those with type 2 diabetes and chronic kidney disease, and is associated with significant mortality and morbidity. In the CREDENCE trial, canagliflozin reduced the risk of hospitalization for heart failure (HHF) or cardiovascular (CV) death by 31%. In the current analysis we sought to determine whether the effect of canagliflozin on HHF/CV death differed in subgroups defined by key baseline participant characteristics. Cox regression models were used to estimate hazard ratios and 95% confidence intervals. Canagliflozin was associated with a reduction in the relative risk of HHF/CV death regardless of age, sex, history of heart failure or CV disease, and the use of loop diuretics or glucagon‐like peptide‐1 receptor agonists (all p interaction > .114). The absolute benefit of canagliflozin was greater in those at highest baseline risk, such as those with CV disease (50 fewer events/1000 patients treated over 2.5 years vs. 20 fewer events in those without CV disease) or advanced kidney disease (estimated glomerular filtration rate [eGFR] 30–45 mL/min/1.73m 2 : 61 events prevented/1000 patients treated over 2.5 years vs. 23 events in eGFR 60–90 mL/min/1.73m 2 ). Canagliflozin consistently reduces the proportional risk of HHF/CV death across a broad range of subgroups with greater absolute benefits in those at highest baseline risk.
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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 | 0.013 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".