Empagliflozin as adjunct to insulin in patients with type 1 diabetes: a 4‐week, randomized, placebo‐controlled trial ( <scp>EASE</scp> ‐1)
Bibliographic record
Abstract
AIMS: To investigate the pharmacodynamics, efficacy and safety of empagliflozin as adjunct to insulin in patients with type 1 diabetes. METHODS: A total of 75 patients with glycated haemoglobin (HbA1c) concentrations of ≥7.5 to ≤10.5% (≥58 to ≤91 mmol/mol) were randomized to receive once-daily empagliflozin 2.5 mg, empagliflozin 10 mg, empagliflozin 25 mg, or placebo as adjunct to insulin for 28 days. Insulin dose was to be kept as stable as possible for 7 days then adjusted, at the investigator's discretion, to achieve optimum glycaemic control. The primary exploratory endpoint was change from baseline in 24-h urinary glucose excretion (UGE) on day 7. RESULTS: Empagliflozin significantly increased 24-h UGE versus placebo on days 7 and 28. On day 28, adjusted mean differences with empagliflozin versus placebo in changes from baseline in: HbA1c were -0.35 to -0.49% (-3.8 to -5.4 mmol/mol; all p < 0.05 vs. placebo); total daily insulin dose -0.07 to -0.09 U/kg (all p<0.05 vs placebo); and weight were -1.5 to -1.9 kg (all p < 0.001 vs. placebo). In the placebo, empagliflozin 2.5, 10 and 25 mg groups, respectively, adverse events were reported in 94.7, 89.5, 78.9 and 100.0% of patients, and the rate of symptomatic hypoglycaemic episodes with glucose ≤3.0 mmol/l not requiring assistance was 1.0, 0.4, 0.5 and 0.8 episodes per 30 days. CONCLUSIONS: In patients with type 1 diabetes, empagliflozin for 28 days as adjunct to insulin increased UGE, improved HbA1c and reduced weight with lower insulin doses compared with placebo and without increasing hypoglycaemia.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 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) | 0.006 | 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".