Superior efficacy of insulin degludec/liraglutide versus insulin glargine U100 as add‐on to sodium‐glucose co‐transporter‐2 inhibitor therapy: A randomized clinical trial in people with uncontrolled type 2 diabetes
Bibliographic record
Abstract
AIM: To investigate the efficacy and safety of insulin degludec/liraglutide (IDegLira) versus insulin glargine 100 units/mL (IGlar U100) as add-on to sodium-glucose co-transporter-2 (SGLT2) inhibitor therapy. MATERIALS AND METHODS: and inadequately controlled type 2 diabetes (T2D) on SGLT2 inhibitor ± oral antidiabetic drugs were randomized 1:1 to once-daily IDegLira or IGlar U100, both as add-on to existing therapy. The primary endpoint was change in HbA1c from baseline to week 26. RESULTS: A total of 210 participants were randomized to each treatment arm. Mean HbA1c reductions were 21 mmol/mol (1.9%-points) with IDegLira and 18 mmol/mol (1.7%-points) with IGlar U100; confirming non-inferiority (P < 0.0001) and superiority of IDegLira (difference in HbA1c change -3.90 mmol/mol; 95% confidence interval [CI] -5.45; -2.35 (-0.36%-points; 95% CI -0.50, -0.21)). Superiority for IDegLira over IGlar U100 was also confirmed for: body weight (difference -1.92 kg; 95% CI -2.64, -1.19); severe or blood-glucose-confirmed symptomatic hypoglycaemia (rate ratio 0.42; 95% CI 0.23, 0.75); total daily insulin dose (difference -15.37 U; 95% CI -19.60, -11.13). The overall treatment-emergent adverse event rate was higher with IDegLira as a result of higher increased lipase and nausea rates. CONCLUSIONS: The favourable safety and efficacy profile of IDegLira in people with uncontrolled T2D on SGLT2 inhibitors, and lower weight gain and hypoglycaemia risk versus IGlar U100, suggest that clinicians should consider IDegLira initiation in this population.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 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 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".