Early use of glucagon-like peptide-1 receptor agonists (GLP-1 RAs) in Type 2 diabetes
Bibliographic record
Abstract
OBJECTIVE: To evaluate the efficacy and safety of the available glucagon-like peptide-1 receptor agonists (GLP-1 RAs) exenatide and liraglutide (marketed as Byetta * and Victoza † , respectively) in first- or second-line pharmacotherapy for type 2 diabetes (T2D), described here as 'early use'. RESEARCH DESIGN AND METHODS: MEDLINE, EMBASE and Google Scholar databases were queried for clinical trial reports using the terms incretin, GLP-1, exenatide and liraglutide. Relevant articles were those that employed these agents in treatment-naïve patients with T2D and in patients who had failed on metformin monotherapy. Additional targeted searches were conducted on diabetes treatment guidelines and on the range of physiological responses to GLP-1 RAs. Most evidence is level I and II. RESULTS: Effective therapy for T2D should be implemented early in the course of this progressive disease. The recently revised 2013 Canadian Diabetes Association (CDA) guidelines now identify the GLP-1 RAs among various injected and oral agents recommended for the management of T2D. The rationale for early use of GLP-1 RAs in T2D management is manifold: these agents offer effective management of hyperglycemia in early-stage T2D, minimal risk of hypoglycemia, weight loss, improvement in multiple non-glycemic cardiovascular risk factors, and potential enhancement of patient adherence to antihyperglycemic treatment. Available data from clinical trials support second-line use of GLP-1 RAs among patients who fail on metformin, as well as first-line use of these agents in a subset of T2D patients. CONCLUSIONS: The ability to achieve glycemic targets using GLP-1 RAs while simultaneously avoiding hypoglycemia and weight gain could provide substantial reassurance to physicians and patients who might otherwise resist the transition to injected therapies. Exenatide and liraglutide represent appropriate second-line choices for pharmacological treatment of T2D, as indicated in the 2013 CDA guidelines.
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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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".