Are Post Hoc Analyses on Subgroups Sufficient to Support New Treatment Algorithms of Heart Failure? The Case of SGLT2 Inhibitors Associated with Sacubitril/Valsartan
Bibliographic record
Abstract
The use of sodium glucose cotransporter 2 inhibitors (SGLT2i) in heart failure (HF) with reduced ejection fraction (HFrEF) has been strongly supported by the results of recent randomized clinical trials. Upon this evidence, international recommendations and consensus documents propose the inclusion of SGLT2i among the first-line classes for HFrEF management. Subsequent analyses of treatment subgroups have been performed to investigate the effects of SGLT2i in patients treated with first-line classes including sacubitril/valsartan (Sac/Val), showing a consistent reduction of cardiovascular outcomes with a good safety profile of SGLT2i in combination with the other classes. Accordingly, SGLT2i are recommended also in combination with Sac/Val. This association, however, may require caution before being translated into guideline-directed medical therapy in clinical practice, since the proportion of patients receiving Sac/Val and SGLT2i in the available studies was poorly represented. In order to support an effective and safe sequencing or a simultaneous initiation of these 2 drug classes, pragmatic and real-world clinical studies would be helpful.
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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 | 0.052 | 0.213 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".