Composite endpoints in trials of type‐2 diabetes
Bibliographic record
Abstract
Composite endpoints (CEPs) are being used more frequently as outcomes for trials of drugs in type-2 diabetes. We reviewed the literature to determine how CEPs have been used to date in trials of drugs for type-2 diabetes. A systematic search was undertaken on Medline, Embase and Cochrane databases and Clinicaltrials.gov for randomized controlled trials of currently marketed agents including SGLT-2 inhibitors (dapagliflozin), GLP-1 agonists (exenatide, liraglutide) and DPP-4 inhibitors (linagliptin, saxagliptin, sitagliptin and vildagliptin). CEPs used were identified as well as numbers and percentages of patients achieving each. Thirty-six studies were identified that reported results on ≥1 CEP; 15 different CEPs were reported (7 with 2 components, 8 with 3 components). All CEPs addressed goals recommended by the American Diabetes Association (ADA). All included HbA1c<7%; other endpoints measured weight, blood pressure and hypoglycaemic events. Results were obtained for CEPs from 6 months to 2 years. Rates of achieving CEPs decreased with increasing numbers of components and outcomes assessed. CEPs are becoming used as indicators of clinical outcomes in type-2 diabetes trials, but are still not common. More research is required to identify optimal CEPs. Standardization of outcomes and their reporting is needed.
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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 | 0.048 | 0.084 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.007 |
| Bibliometrics | 0.013 | 0.016 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".