Insulin degludec/liraglutide (IDegLira) was effective across a range of dysglycaemia and body mass index categories in the <scp>DUAL V</scp> randomized trial
Bibliographic record
Abstract
This study assessed the efficacy of insulin degludec/liraglutide ( IDegLira ) vs insulin glargine U 100 ( IG lar) across categories of baseline glycated haemoglobin ( HbA1c ; ≤7.5%, >7.5% to ≤8.5% and >8.5%), body mass index ( BMI ; <30, ≥30 to <35 and ≥35 kg/m 2 ) and fasting plasma glucose ( FPG ; <7.2 and ≥7.2 mmol/L) in patients with type 2 diabetes ( T2D ) uncontrolled on basal insulin, using post hoc analyses of the DUAL V 26‐week trial. With IDegLira , mean HbA1c was reduced across all baseline HbA1c (1.0%‐2.5%), FPG (1.5%‐1.9%) and BMI categories (1.8%‐1.9%), with significantly greater reductions compared with IGlar U100. For all HbA1c , FPG and BMI categories, IDegLira resulted in weight loss and IGlar U100 in weight gain; hypoglycaemia rates were lower for IDegLira vs IGlar U100. More patients achieved HbA1c <7% with IDegLira than IGlar U100 across all HbA1c (59%‐87% vs 31%‐66%), FPG (71%‐74% vs 40%‐51%) and BMI categories (71%‐73% vs 40%‐54%). IDegLira improved glycaemic control and induced weight loss in patients with T2D previously uncontrolled on basal insulin, across the categories of baseline HbA1c , FPG or BMI that were tested.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".