SGLT2 inhibitors
Bibliographic record
Abstract
PURPOSE OF REVIEW: To address common concerns regarding sodium-glucose cotransporter 2 (SGLT2) inhibitor use for patients with type 2 diabetes mellitus (T2DM) in cardiovascular practice. RECENT FINDINGS: SGLT2 inhibitors provide glycemic control and improve cardiovascular and renal endpoints in T2DM. Cardiovascular outcome trials have demonstrated sustained cardiovascular, heart failure and renal benefits independent of glycemic control, which persist down to an eGFR of 30 ml/min/1.73 m. SGLT2 inhibitors can be safely administered alongside common diuretics, and routine monitoring of renal function is advised at initiation of therapy, particularly for patients on loop diuretics. Mild initial reductions in eGFR are expected, usually stabilizing over time. The most common adverse effect noted with SGLT2 inhibitors is genital mycotic infections, primarily in women. Less common, but concerning effects associated with canagliflozin include increased risk of fractures and lower limb amputations, particularly in patients with previous amputation history. Overall, SGLT2 inhibitors are well tolerated and effective adjuncts to diabetic treatment, for which the benefits seem to outweigh the risks. SUMMARY: The care of patients with T2DM requires an interdisciplinary team approach, within which the role of cardiologists is expanding. SGLT2 inhibitors are an encouraging treatment option for achieving glycemic control, whilst also improving cardiovascular and renal 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.011 | 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".