Comparative efficacy and safety of antihyperglycemic drug classes for patients with type 2 diabetes following failure with metformin monotherapy: A systematic review and network meta‐analysis of randomized controlled trials
Bibliographic record
Abstract
AIMS: To compare the efficacy and safety of antihyperglycemic agents, taken in combination with metformin, for the treatment of type 2 diabetes mellitus (T2DM). METHODS: A previous 2016 comprehensive search of Ovid MEDLINE, PubMed, and Cochrane CENTRAL was updated to October 2018, and a systematic review and network meta-analysis (NMA) was conducted. Randomized controlled trials (RCTs) of patients with T2DM taking an antihyperglycemic agent in combination with metformin were included. Bayesian NMA was performed to assess the relative efficacy and safety of the antihyperglycemic classes. RESULTS: In total, 204 RCTs were included, which assessed the efficacy and safety of eight antihyperglycemic drug classes (i.e., sulfonylureas, meglitinides, alpha-glucosidase inhibitors, thiazolidinediones, basal and biphasic insulin, dipeptidyl peptidase 4 inhibitors, glucagon-like-peptide-1 receptor agonists and sodium-glucose cotransport-2 inhibitors). All drug classes significantly reduced haemoglobin A1c (HbA1c) compared to metformin monotherapy (mean reduction from 0.50 to 0.92). The drug classes varied in their relative effects on hypoglycemia, body weight, body mass index, systolic and diastolic blood pressure, total cholesterol, high and low density lipoprotein cholesterol, and the classes had differing safety profiles on total adverse events, urogenital adverse events, heart failure, serious adverse events, and withdraw due to adverse events. CONCLUSIONS: All eight antihyperglycemic drug classes, taken in combination with metformin, reduced HbA1c levels; however, the effects of the agents on other outcomes varied among the classes.
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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 | 0.020 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.035 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".