Safety and efficacy of linagliptin in patients with type 2 diabetes mellitus and coronary artery disease: Analysis of pooled events from 19 clinical trials
Bibliographic record
Abstract
AIMS: To examine the safety and efficacy of linagliptin in patients with type 2 diabetes mellitus (T2DM) and coronary artery disease (CAD) using pooled data from the global clinical trials program. METHODS: Patient-level data were pooled from randomized, placebo-controlled clinical trials of linagliptin (5mg, monotherapy or combination therapy). Safety/efficacy analyses were conducted for patients with CAD and ≥12 and ≥24weeks of treatment, respectively. RESULTS: The safety analysis included 19 trials (linagliptin, n=451; placebo, n=272) and the efficacy analysis, 12 trials (linagliptin, n=328; placebo, n=198); mean (± standard deviation) exposure to study treatment was 212 (144) days linagliptin and 245 (171) days placebo. Occurrence of cardiac adverse events (AEs) was similar for linagliptin- and placebo-treated patients (9.1% and 9.2%, respectively); exposure-adjusted incidence rates (per 100 patient-years) were 16.6 and 14.0, respectively. Overall incidence of AEs was numerically lower with linagliptin than placebo. After 24weeks, mean adjusted change (standard error) from baseline glycosylated hemoglobin was -0.64% (0.04) with linagliptin vs. -0.08% (0.05) with placebo (P<.001). CONCLUSIONS: This comprehensive pooled analysis showed that addition of linagliptin to treatment regimens of patients with T2DM and CAD was not associated with an increased incidence of cardiac AEs, was well tolerated, and was effective.
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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 | 0.042 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.021 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".