Sodium-Glucose Cotransport Inhibition With Dapagliflozin in Type 2 Diabetes
Bibliographic record
Abstract
OBJECTIVE: Dapagliflozin, a novel inhibitor of renal sodium-glucose cotransporter 2, allows an insulin-independent approach to improve type 2 diabetes hyperglycemia. In this multiple-dose study we evaluated the safety and efficacy of dapagliflozin in type 2 diabetic patients. RESEARCH DESIGN AND METHODS: Type 2 diabetic patients were randomly assigned to one of five dapagliflozin doses, metformin XR, or placebo for 12 weeks. The primary objective was to compare mean change from baseline in A1C. Other objectives included comparison of changes in fasting plasma glucose (FPG), weight, adverse events, and laboratory measurements. RESULTS: After 12 weeks, dapagliflozin induced moderate glucosuria (52-85 g urinary glucose/day) and demonstrated significant glycemic improvements versus placebo (DeltaA1C -0.55 to -0.90% and DeltaFPG -16 to -31 mg/dl). Weight loss change versus placebo was -1.3 to -2.0 kg. There was no change in renal function. Serum uric acid decreased, serum magnesium increased, serum phosphate increased at higher doses, and dose-related 24-h urine volume and hematocrit increased, all of small magnitude. Treatment-emergent adverse events were similar across all groups. CONCLUSIONS: Dapagliflozin improved hyperglycemia and facilitates weight loss in type 2 diabetic patients by inducing controlled glucosuria with urinary loss of approximately 200-300 kcal/day. Dapagliflozin treatment demonstrated no persistent, clinically significant osmolarity, volume, or renal status changes.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".