Sodium-Glucose Cotransporter-2 Inhibitors in Nephrology Practice: A Narrative Review
Bibliographic record
Abstract
PURPOSE OF THE REVIEW: Sodium-glucose cotransporter-2 inhibitors (SGLT2is) are recommended for eligible patients with type 2 diabetes for the secondary prevention of adverse cardiovascular and kidney disease outcomes. Patients with type 2 diabetes and albuminuric chronic kidney disease, a history of atherosclerotic cardiovascular disease, and/or heart failure with reduced ejection fraction should be assessed for the use of these therapies. SOURCES OF INFORMATION: The sources include published clinical trials with SGLT2is, with a focus on cardiovascular safety studies and kidney protection trials. METHODS: Information was gathered via a review of relevant literature and clinical practice guidelines, incorporated with real-life clinical experience. KEY FINDINGS: Clinicians prescribing these agents must be familiar with the benefits of SGLT2is on cardiovascular and renal endpoints, and with adverse effects of SGLT2is, including mycotic genital infections and diabetic ketoacidosis. Primary care physicians and specialists should know how to adjust antihypertensive, antiglycemic, and diuretic agents. With the results of completed cardiovascular outcome trials and the Canagliflozin and Renal Outcomes in Type 2 Diabetes and Nephropathy trial, nephrologists specifically have a unique opportunity to impact the safe, effective, and equitable implementation of SGLT2is into clinical practice. LIMITATIONS: Further work is needed in specific patient subgroups, including patients with chronic kidney disease stages IV and V, patients with kidney disease but lower levels of albuminuria, and in patients without diabetes.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".