Real-Time Use of SGLT2i Verified in Pre-dialysis: The RSVP Cross-sectional Study
Bibliographic record
Abstract
BACKGROUND: The use of sodium-glucose cotransporter 2 inhibitors (SGLT2i) in nephrology practice is increasingly becoming standard of care in patients with diabetes or those with proteinuria. OBJECTIVES: The primary outcome was to identify the proportion of pre-dialysis patients with chronic kidney disease (CKD) G3a, G3b, or G4 prescribed an SGLT2i and describe their characteristics. METHODS: This was a retrospective, multicentric, cross-sectional study of patients with CKD followed at 4 pre-dialysis clinics in the province of Quebec, Canada. We collected data of multiple covariates associated with prescribing SGLT2i in patients over 18 years of age with CKD G3a, G3b, or G4. We then performed a multivariate logistic regression to assess their associations. RESULTS: Of the 874 patients included, 22.7% were prescribed an SGLT2i. Factors most strongly associated included male sex (odds ratio [OR] = 4.88, 95% CI = 2.38-10.03), being prescribed metformin (OR = 4.30, 95% CI = 2.23-8.31), having type 2 diabetes (OR = 4.00, 95% CI = 1.86-8.62), or having an albumin-to-creatinine ratio greater than 300 mg/g (OR = 1.84, 95% CI = 1.08-3.14). The majority of patients (60.4%) had their SGLT2i initiated by the pre-dialysis clinic and the most frequent adverse event was an initial increase in serum creatinine 1 week after starting treatment (33.9%). CONCLUSION AND RELEVANCE: An increasing number of patients with CKD are being prescribed SGLT2i. Nonetheless, significant disparities in sex, severity of disease, and comorbidities remain. We suggest that specific strategies be put in place to promote prescribing of SGLT2i in women and other at-risk populations, in particular among nephrology teams, to improve patient care.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".