Fixed‐ratio combination of insulin degludec and liraglutide (IDegLira) improves cardiovascular risk markers in patients with type 2 diabetes uncontrolled on basal insulin
Bibliographic record
Abstract
In this post hoc analysis we investigated the effects of insulin degludec/liraglutide fixed-ratio combination (IDegLira) versus comparators on cardiovascular (CV) risk markers in participants in the DUAL II (vs. insulin degludec), DUAL V (vs. insulin glargine 100 units/mL) and DUAL VII (vs. basal-bolus therapy) trials, grouped by sex, age (<65 years, ≥65 years) and diabetes duration (<10 years, ≥10 years). Treatment contrasts were in favour of IDegLira in many subgroups for changes from baseline in glycated haemoblogin (DUAL II, DUAL V), body weight (all three trials), systolic blood pressure (BP; all three trials), HDL cholesterol (DUAL VII) and LDL cholesterol (DUAL II, DUAL V). Higher heart rates were seen with IDegLira versus comparators (all three trials) plus significantly higher diastolic BP in men (DUAL V). Differences in treatment effect were seen between sexes in waist circumference (DUAL II), systolic BP (DUAL II, DUAL V) and triglycerides (DUAL VII), and between diabetes durations in LDL cholesterol (DUAL V). In conclusion, IDegLira is associated with a general improvement in CV risk markers compared with basal insulin or basal-bolus therapy after 26 weeks of treatment.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".