The Synergistic Effect of Miglitol Plus Metformin Combination Therapy in the Treatment of Type 2 Diabetes
Bibliographic record
Abstract
OBJECTIVE: To investigate the efficacy and safety of miglitol in combination with metformin in improving glycemic control in outpatients in whom type 2 diabetes is insufficiently controlled by diet alone. RESEARCH DESIGN AND METHODS: In this multicenter, double-blind, placebo-controlled study, 324 patients with type 2 diabetes were randomized, after an 8-week placebo run-in period, to treatment with either placebo, miglitol alone, metformin alone, or miglitol plus metformin for 36 weeks. The miglitol was titrated to 100 mg three times a day and metformin was administered at 500 mg three times a day. The primary efficacy criterion was change in HbA(1c) from baseline to the end of treatment. Secondary parameters included changes in fasting and postprandial plasma glucose and insulin levels, serum triglyceride levels, and responder rate. RESULTS: A total of 318 patients were valid for intent-to-treat analysis. A reduction in mean placebo-subtracted HbA(1c) of -1.78% was observed with miglitol plus metformin combination therapy, which was significantly different from treatment with metformin alone (-1.25; P = 0.002). Miglitol plus metformin also resulted in better metabolic control than metformin alone for fasting plasma glucose (-44.8 vs. -20.4 mg/dl; P = 0.0025), 2-h postprandial glucose area under the curve (-59.0 vs. -18.0 mg/dl; P = 0.0001), and responder rate (70.6 vs. 45.52%; P = 0.0014). All therapies were well tolerated. CONCLUSIONS: In type 2 diabetic patients, miglitol in combination with metformin gives greater glycemic improvement than metformin monotherapy.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".