Heart Failure Outcomes in Clinical Trials of Glucose-Lowering Agents in Patients with Diabetes
Bibliographic record
Abstract
Diabetes is a major risk factor for heart failure (HF). Patients with diabetes have a high incidence of both clinical HF and subclinical LV dysfunction. Although intensive glucose lowering does not appear to impact on HF outcomes, the choice of glucose-lowering agents plays an important role in the development of HF and related cardiovascular outcomes. Whilst metformin and insulin appear to have little impact on HF progression, the role of sulphonylurea agents in this patient population remains uncertain. Thiazolidinediones (TZDs) are associated with a significant risk of HF progression and are best avoided in patients at risk. The incretin-based therapies (GLP agonists and DPP-4 inhibitors) are generally not associated with any HF interaction. However, a small increase in HF admissions was observed with the DPP-4 inhibitor saxagliptin. The GLP-1 agonist liraglutide was recently shown to reduce cardiovascular and all-cause mortality, yet hospitalization for HF was not significantly reduced. The SGLT2 inhibitor empagliflozin was shown to reduce HF admissions and cardiovascular mortality in patients with prior cardiovascular disease including HF. These recent data showing improved outcomes with a glucose-lowering category provide a novel strategy to improve survival and reduce morbidity in diabetic patients at high cardiovascular disease 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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 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 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".