FLOW: Effects of Semaglutide on Kidney Failure Using the Kidney Failure Risk Equation
Bibliographic record
Abstract
Background: In FLOW, once-weekly subcutaneous semaglutide 1.0 mg reduced the risk of major kidney outcomes in people with type 2 diabetes and chronic kidney disease. This post hoc analysis evaluated the effect of semaglutide on risk of kidney failure using the kidney failure risk equation (KFRE). Methods: Participants received semaglutide or placebo. The main outcome of this analysis was the 5-year risk of kidney failure (estimated glomerular filtration rate [eGFR] <15 mL/min/1.73 m2 or kidney replacement therapy) predicted with the four-variable KFRE, using creatinine-based eGFR (calculated using CKD-EPI 2009), urine albumin-to-creatinine ratio, age, and sex in a Cox regression model. Results: In total, 3533 participants were followed for (median) 3.4 years. At baseline, mean (standard deviation) KFRE scores were 0.12 (0.17) and 0.11 (0.15) in semaglutide and placebo arms, respectively. KFRE scores increased over time in both arms but were consistently lower for semaglutide (Figure A). Separation of semaglutide and placebo arms was observed from week 26, and at week 104, the estimated treatment difference was −0.06 (95% confidence interval −0.07, −0.05; p<0.0001). Modest concordance (C-index=0.58) was observed between the predicted event rate for time to kidney failure and KFRE score at baseline (Figure B). Conclusion: Predicted risk of kidney failure increased less with semaglutide vs placebo during the FLOW trial, supporting the results of the primary FLOW analyses. Funding: Commercial Support - The FLOW trial was funded by Novo Nordisk A/S
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".