Safety of sitagliptin in patients with type 2 diabetes and chronic kidney disease: outcomes from <scp>TECOS</scp>
Bibliographic record
Abstract
AIMS: To characterize the incidence of diabetes-associated complications and assess the safety of sitagliptin in participants with chronic kidney disease (CKD) in the Trial Evaluating Cardiovascular Outcomes with Sitagliptin (TECOS). MATERIALS AND METHODS: ) vs those without CKD. Within the CKD cohort, the same analyses were performed, comparing sitagliptin- and placebo-assigned participants. Baseline characteristics were summarized for all participants, and serious adverse events were analysed in those who received at least 1 dose of study medication. Adverse events of interest and diabetes complications were summarized for the intention-to-treat population. RESULTS: CKD was present in 3324 (23%) participants at entry into TECOS. The mean (SD) age for this CKD cohort was 68.8 (7.9) years, mean diabetes duration was 13.7 (9.0) years, and 62% were men. Incidences of serious adverse events, malignancy, bone fracture, severe hypoglycaemia and most categories of diabetes complications were higher in the CKD cohort compared with those without CKD. Over ~2.8 median years of follow-up, CKD participants assigned to sitagliptin had rates of diabetic eye disease, diabetic neuropathy, renal failure, malignancy, bone fracture, pancreatitis and severe hypoglycaemia similar to those of placebo-assigned participants. CONCLUSIONS: Participants in TECOS with CKD had higher incidences of serious adverse events and diabetes complications than those without CKD. Treatment with sitagliptin was generally well tolerated, with no meaningful differences in safety outcomes observed between those with CKD assigned to sitagliptin or placebo.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".