A predictive model for estimating protection against CKD and CVD with SGLT2 inhibition in patients with diabetes
Bibliographic record
Abstract
Diabetes affects >450 million people worldwide. Elevated blood glucose caused by diabetes can lead to chronic kidney disease (CKD), which increases the risk of end‐stage kidney disease (ESKD) requiring dialysis or a kidney transplant. In type 2 diabetes (T2D), medications called sodium‐glucose cotransporter‐2 (SGLT2) inhibitors have become part of standard of the care for improving glycemic control by promoting urine glucose excretion. These drugs reduce urinary albumin excretion (quantified as urinary albumin‐to‐creatinine ratio, “UACR”, an effect linked with kidney protection), slow CKD progression and delay ESKD, with similar benefits in males and females. In people with T2D, SGLT2 inhibitors also reduce heart failure and cardiovascular events by 20‐40%. But in people with type 1 diabetes (T1D), there are no studies evaluating SGLT2 inhibitors in patients at the highest risk of ESKD or cardiovascular disease (CVD). Building on the benefits of SGLT2 inhibitors in people with T2D, we seek to evaluate the efficacy and mechanisms in people with T1D and CKD. We hypothesize that SGLT2 inhibition will slow loss of kidney function (glomerular filtration rate, or “GFR”) in people with T1D and CKD. To test that hypothesis, we analyze clinical data for a cohort of patients with diabetes and kidney disease, and identify key features that determine the rate of progression of CKD. We then develop personalized assessment tools that yield estimates for protection against CKD and CVD with SGLT2 inhibition in patients with T1D or T2D.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".