Does the use of incretin‐based medications increase the risk of cancer in patients with type‐2 diabetes mellitus?
Bibliographic record
Abstract
PURPOSE: Incretin-based medications are a novel class of agents for the treatment of type-2 diabetes mellitus (DM2). The safety profile of these medications is not firmly established, and concerns have been raised about their potential carcinogenicity. The objective of our study was to produce new evidence on the effect of incretin-based medications on cancer risk in patients with DM2. METHODS: We conducted a "retrospective cohort" study with data from the Clinical Practice Research Datalink and the Hospital Episodes Statistics in the UK. New users of either an incretin-based medication (n = 18 885) or a sulfonylurea medication (n = 36 929) between 2007 and 2013 were identified and followed for up to 8 years. Cox proportional-hazards models were used to estimate the quasi-intention-to-treat and quasi-per-protocol hazard-ratios for the association between incretin-based medications with cancer while adjusting for potential confounders. RESULTS: The adjusted hazard ratio (95% confidence interval) for use of incretin-based medications versus use of sulfonylurea medications for the overall-cancer outcome was 0.97 (0.90, 1.05) in the quasi-intention-to-treat analysis and 0.90 (0.81, 1.00) in the quasi-per-protocol analysis. In both analyses, the hazard-ratio functions over the 8-year follow-up seemed fairly constant, and the 8-year cumulative-risk functions in the two subcohorts were similar. CONCLUSIONS: Our study suggests that the use of incretin-based medications in patients with DM2 does not increase the risk of cancer relative to the use of sulfonylurea medications, at least in the first several years of the use. Further research is needed to assess long-term effects of the use of incretin-based medications on cancer risk.
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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 | 0.011 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".