Empagliflozin improves glycaemic and weight control as add‐on therapy to pioglitazone or pioglitazone plus metformin in patients with type 2 diabetes: a 24‐week, randomized, placebo‐controlled trial
Bibliographic record
Abstract
AIMS: This study investigated the efficacy and tolerability of empagliflozin as add-on to pioglitazone ± metformin in patients with type 2 diabetes (T2DM). METHODS: Patients with HbA1c ≥7 and ≤10% were randomized and treated with once daily empagliflozin 10 mg (n = 165), empagliflozin 25 mg (n = 168) or placebo (n = 165) as add-on to pioglitazone ± metformin for 24 weeks. Endpoints included changes from baseline in HbA1c (primary endpoint), fasting plasma glucose (FPG) and body weight at week 24. RESULTS: Adjusted mean ± standard error changes in HbA1c were -0.6 ± 0.07% and -0.7 ± 0.07% with empagliflozin 10 mg and 25 mg, respectively, vs. -0.1 ± 0.07% with placebo (both p < 0.001). More patients with HbA1c ≥7% at baseline achieved HbA1c <7% with empagliflozin 10 mg (23.8%) and 25 mg (30.0%) vs. placebo (7.7%) (both p < 0.001). FPG decreased with empagliflozin (-0.94 mmol/l for 10 mg and -1.22 mmol/l for 25 mg) and increased with placebo (+0.36 mmol/l; both p < 0.001). Adjusted mean ± standard error changes in weight were -1.62 ± 0.21 kg and -1.47 ± 0.21 kg with empagliflozin 10 mg and 25 mg, respectively, vs. +0.34 ± 0.21 kg with placebo (both p < 0.001). Similar proportions of patients reported adverse events with empagliflozin (67.3-71.4%) and placebo (72.7%). Confirmed hypoglycaemia was reported by 1.2-2.4% of patients on empagliflozin and 1.8% on placebo. CONCLUSION: Empagliflozin 10 mg and 25 mg once daily for 24 weeks as add-on to pioglitazone ± metformin reduced HbA1c, FPG and weight and were well tolerated in patients with T2DM.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".