Beyond glycaemic control: A cross‐over, double‐blinded, 24‐week intervention with liraglutide in type 1 diabetes
Bibliographic record
Abstract
AIMS: To investigate the effects of 24 weeks of treatment with liraglutide added to basal/bolus insulin on anthropometric and metabolic parameters in overweight participants with type 1 diabetes. METHODS: In a double-blinded cross-over fashion, 15 participants were randomly assigned (1:1) to receive placebo (saline solution) or liraglutide for 24 weeks including a 1-month titration period from 0.6 to 1.2 to 1.8 mg, in addition to their insulin. The treatment was followed by a 1-month wash-out period. Participants were then assigned to the other treatment for another 24 weeks. Paired rank tests were used to compare the metabolic parameters. RESULTS: There was no treatment effect on HbA1c nor on insulin dose. Heart rate was increased by about 8 beats per minute with liraglutide. There were significant reductions in metabolic measures: weight, body mass index, waist and hip circumferences, body fatness, computed tomography scan abdominal and mid-thigh measurements, systolic and diastolic blood pressures (all P ≤ .05). There was no increase in time spent in hypoglycaemia with liraglutide. CONCLUSIONS: The addition of liraglutide to basal/bolus insulin therapy for 24 weeks in overweight/obese individuals with type 1 diabetes improved the anthropometric and metabolic profiles without an increase in hypoglycaemia. Clinical Trials.gov No: NCT01787916.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".