Incretin‐based medications for type 2 diabetes: an overview of reviews
Bibliographic record
Abstract
AIMS: To summarize evidence from and assess the quality of published systematic reviews evaluating the safety, efficacy and effectiveness of incretin-based medications used in the treatment of type 2 diabetes. METHODS: We identified systematic reviews of randomized controlled trials or observational studies published in any language that evaluated the safety and/or effectiveness of glucagon-like peptide-1 (GLP-1) receptor agonists or dipeptidyl-peptidase-4 (DPP-4) inhibitors. Data sources used include the Cochrane Library, PubMed, EMBASE, Web of Science, International Pharmaceutical Abstracts, table of contents of diabetes journals, and hand-searching of reference lists and clinical practice guidelines. The methodological quality of systematic reviews was independently assessed by two reviewers using the Assessment of Multiple Systematic Reviews (AMSTAR) checklist. Our study protocol was registered with PROSPERO (2013:CRD42013005149). The primary outcomes were pooled treatment effect estimates for glycaemic control, macrovascular and microvascular complications, and hypoglycaemic events. RESULTS: We identified 467 unique citations of which 84 systematic reviews met our inclusion criteria. There were 51 reviews that evaluated GLP-1 receptor agonists and 64 reviews that evaluated DPP-4 inhibitors. The median (interquartile range) AMSTAR score was 6 (3) out of 11 for quantitative and 1 (1) for non-quantitative reviews. Among the 66 quantitative systematic reviews, there were a total of 718 pooled treatment effect estimates reported for our primary outcomes and 1012 reported pooled treatment effect estimates for secondary outcomes. CONCLUSIONS: Clinicians and policy makers, when using the results of systematic reviews to inform decision-making with regard to round clinical care or healthcare policies for incretin-based medications, should consider the variability in quality of reviews.
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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 | 0.021 | 0.070 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.010 |
| Bibliometrics | 0.024 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".