Effects of once‐weekly dulaglutide on kidney function in patients with type 2 diabetes in phase <scp>II</scp> and <scp>III</scp> clinical trials
Bibliographic record
Abstract
Dulaglutide is a once‐weekly glucagon‐like peptide‐1 receptor agonist approved for the treatment of type 2 diabetes (T2D). Integrated data from 9 phase II and III trials in people with T2D (N = 6005) were used to evaluate the effects of dulaglutide on estimated glomerular filtration rate ( eGFR [Chronic Kidney Disease Epidemiology Collaboration]), urine albumin‐to‐creatinine ratio ( UACR ) and kidney adverse events ( AEs ). No significant differences in eGFR were observed during treatment for dulaglutide vs placebo, active comparators or insulin glargine (mean ± standard deviation values: dulaglutide vs placebo: 87.8 ± 17.7 vs 88.2 ± 17.9 mL /min/1.73 m 2 , P = .075; dulaglutide vs active comparators: 89.9 ± 16.7 vs 88.8 ± 16.3 mL /min/1.73 m 2 , P = .223; and dulaglutide vs insulin glargine: 85.9 ± 18.2 vs 83.9 ± 18.6 mL /min/1.73 m 2 , P = .423). Lower UACR values were observed for dulaglutide vs placebo, active comparators and insulin glargine (at 26 weeks, median [ Q1‐Q3 ] values were: dulaglutide vs placebo: 8.0 [4.4‐20.4] vs 8.0 [4.4‐23.9] mg/g, P = .023; dulaglutide vs active comparators: 8.0 [4.4‐21.2] vs 8.9 [4.4‐27.4] mg/g, P = .013; and dulaglutide vs insulin glargine: 8.9 [4.4‐29.2] vs 12.4 [5.3‐50.5] mg/g, P = .029). AEs reflecting potential acute renal failure were 3.4, 1.7 and 7.0 events/1000 patient‐years for dulaglutide, active comparators and placebo, respectively. In conclusion, dulaglutide treatment of clinical trial participants with T2D did not affect eGFR and slightly decreased 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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".