A Study of Dapagliflozin in Patients With Type 2 Diabetes Receiving High Doses of Insulin Plus Insulin Sensitizers
Bibliographic record
Abstract
OBJECTIVE: To determine whether dapagliflozin, which selectively inhibits renal glucose reabsorption, lowers hyperglycemia in patients with type 2 diabetes that is poorly controlled with high insulin doses plus oral antidiabetic agents (OADs). RESEARCH DESIGN AND METHODS: This was a randomized, double-blind, three-arm parallel-group, placebo-controlled, 26-center trial (U.S. and Canada). Based on data from an insulin dose-adjustment setting cohort (n = 4), patients in the treatment cohort (n = 71) were randomly assigned 1:1:1 to placebo, 10 mg dapagliflozin, or 20 mg dapagliflozin, plus OAD(s) and 50% of their daily insulin dose. The primary outcome was change from baseline in A1C at week 12 (dapagliflozin vs. placebo, last observation carried forward [LOCF]). RESULTS: At week 12 (LOCF), the 10- and 20-mg dapagliflozin groups demonstrated -0.70 and -0.78% mean differences in A1C change from baseline versus placebo. In both dapagliflozin groups, 65.2% of patients achieved a decrease from baseline in A1C > or =0.5% versus 15.8% in the placebo group. Mean changes from baseline in fasting plasma glucose (FPG) were +17.8, +2.4, and -9.6 mg/dl (placebo, 10 mg dapagliflozin, and 20 mg dapagliflozin, respectively). Postprandial glucose (PPG) reductions with dapagliflozin also showed dose dependence. Mean changes in total body weight were -1.9, -4.5, and -4.3 kg (placebo, 10 mg dapagliflozin, and 20 mg dapagliflozin). Overall, adverse events were balanced across all groups, although more genital infections occurred in the 20-mg dapagliflozin group than in the placebo group. CONCLUSIONS: In patients receiving high insulin doses plus insulin sensitizers who had their baseline insulin reduced by 50%, dapagliflozin decreased A1C, produced better FPG and PPG levels, and lowered weight more than placebo.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".