Efficacy of Sodium-Glucose Cotransporter-2 inhibitors in heart failure patients treated with dual angiotensin receptor blocker-neprilysin inhibitor: An updated meta-analysis
Bibliographic record
Abstract
BACKGROUND: Recent data suggest that the prevalence of heart failure has increased to approximately 23 million people globally. With increasing advancement in pharmacotherapeutics, Sodium-Glucose Cotransporter-2 inhibitors (SGLT2i) have garnered attention among clinicians to treat Heart failure with reduced ejection fraction (HFrEF) in diabetic as well as non-diabetic patients. METHODS: MEDLINE, Scopus, Embase and Cochrane CENTRAL database were searched using relevant keywords and MeSH terms. Studies were considered only if they were randomized in nature and had a sample size >1000 HF patients. RESULTS: Our comprehensive search strategy yielded 864 articles, of which three RCTs met the inclusion criteria with a total population of 9696. Pooled analysis revealed an association between the use of SGLT2i and decreased frequency of primary outcome irrespective of background ARNI use (HR 0.73, 95% CI [0.58-0.93], p = 0.0106; HR 0.73, 95% CI [0.66-0.81], p < 0.0001). CONCLUSION: This meta-analysis provides substantial evidence, to safely use SGLT2i atop ARNI therapy in select HF patients to further improve outcomes.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.035 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".