<scp>S</scp> odium‐glucose co‐transporter‐2 inhibitors are associated with kidney benefits at all degrees of albuminuria: A retrospective cohort study of adults with diabetes
Bibliographic record
Abstract
Abstract Aim To estimate the real‐world effectiveness of sodium‐glucose co‐transporter‐2 inhibitors (SGLT2is) versus dipeptidyl peptidase‐4 inhibitors (DPP4is) at reducing loss of kidney function and adverse kidney events in adults with varying levels of albuminuria. Materials and Methods In this retrospective cohort study using administrative data, we matched new SGLT2i users 1:2 to DPP4i users on diabetes therapy, chronic kidney disease (CKD) stage, albuminuria and time‐conditional propensity score. Albuminuria was defined by spot urine albumin or equivalent as mild, moderate or severe. Linear regression was used to model the estimated glomerular filtration rate (eGFR), and Poisson regression for a composite kidney outcome (> 40% loss of eGFR, kidney replacement therapy or death from kidney causes) and all‐cause mortality. Results SGLT2i users ( n = 19 238, median age 57.9 years, female 40.9%) had mostly nil/mild albuminuria (70.7%). SGLT2is were associated with a 1.36 (95% CI 0.98‐1.74) mL/min/1.73m 2 ( P < .001) acute (≤ 60 days) decline in eGFR, relative to DPP4is. Thereafter, SGLT2is were associated with 1.04 (95% CI 0.93‐1.15) mL/min/1.73m 2 ( P < .001) less annual eGFR loss. SGLT2i users had fewer adverse kidney outcomes (incidence rate ratio [IRR] 0.58 [0.47‐0.71]; P < .001), but not all‐cause mortality (IRR 0.82 [0.66‐1.01]; P = .06). Outcomes were similar considering only those with nil/mild albuminuria. Conclusions SGLT2is may prevent eGFR decline and reduce the risk of adverse kidney events in adults with diabetes and nil or non‐severe albuminuria.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| 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".