SGLT2 Inhibitors in the Prevention and Treatment of Cardiovascular Disease
Bibliographic record
Abstract
Over the last decade, sodium-glucose transport (SGLT2) inhibitors have become one of the most exhaustively studied classes of medications for cardiometabolic diseases. Although multiple trials have established their benefit, one of the crucial tasks of the medical community is to accelerate their effective adoption into clinical practice by identifying the most appropriate patient population who could benefit from it. To date, this class of medications is indicated in patients with symptomatic (NYHA class II-IV) congestive heart failure with preserved or reduced ejection fraction, chronic kidney disease, or type 2 diabetes with cardiovascular (CV) risk factors. This article focuses on best implementing these medications into clinical practice and reviews their proposed mechanisms of action. RésuméAu cours de la dernière décennie, les inhibiteurs du transport du sodium et du glucose (SGLT2) sont devenus l’une des classes de médicaments les plus étudiées dans le traitement des maladies cardiométaboliques. Bien que de multiples essais aient établi leur bénéfice, l’une des tâches cruciales de la communauté médicale est d’accélérer leur adoption effective dans la pratique clinique en identifiant la population de patients la plus appropriée qui pourrait en bénéficier. À ce jour, cette classe de médicaments est indiquée chez les patients souffrant d’insuffisance cardiaque congestive symptomatique (classe II-IV de la NYHA) avec fraction d’éjec-tion préservée ou réduite, de maladie rénale chronique ou de DT2 avec facteurs de risque cardiovasculaire (CV). Cet article se concentre sur la meilleure façon de mettre en œuvre ces médicaments dans la pratique clinique et fournit un examen de leurs mécanismes d’action proposés.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".