Bibliographic record
Abstract
Liraglutide, a once-daily glucagon-like peptide-1 receptor agonist, is approved for use as monotherapy in the USA and Japan (but not in Europe or Canada) and in combination with selected oral agents (all regions) for the treatment of patients with type 2 diabetes. Guidance from local advisory bodies is emerging on the most appropriate place for liraglutide in the treatment pathway. It is apparent from its phase 3 clinical trial programme that liraglutide provides superior glycaemic control compared with that achieved with other antidiabetic agents used early in the treatment pathway (e.g. glimepiride and sitagliptin). Key additional benefits include a low incidence of hypoglycaemia and clinically relevant weight loss, although these benefits may be ameliorated by concomitant sulphonylurea (SU) treatment and, in the case of hypoglycaemia, reduction of the SU dose may be necessary. Overall, the profile of liraglutide is similar and, in some aspects, superior to twice-daily exenatide. The implementation of liraglutide therapy is straightforward, with simple dose titration from the starting dose of 0.6 to 1.2 mg/day after 1 week; some patients may benefit from additional titration to 1.8 mg/day. Treatment is self-administered by subcutaneous injection. This contrasts with other agents used early in the treatment pathway, but clinical data suggest patients' overall treatment satisfaction with liraglutide is similar (1.2 mg) or better (1.8 mg) than that with sitagliptin despite differing administration methods. Some patients may experience nausea when initiating liraglutide treatment, but the titration regimen is designed to improve tolerability and clinical data indicate nausea is transient.
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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 | 0.077 | 0.121 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.015 | 0.008 |
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".