Glucagon like peptide‐1 receptor agonists and the risk of skin cancer among patients with type 2 diabetes: Population‐based cohort study
Bibliographic record
Abstract
AIMS: The objective of this study was to determine whether the use of glucagon-like peptide-1 receptor agonists (GLP-1 RAs) is associated with an increased risk of melanoma and nonmelanoma skin cancer, separately, compared with the use of sulfonylureas among patients with type 2 diabetes. METHODS: Using the United Kingdom Clinical Practice Research Datalink (2007-2019), we assembled two new-user active comparator cohorts. In the first cohort assessing melanoma as the outcome, 11,786 new users of GLP-1 RAs were compared with 208,519 new users of sulfonylureas. In the second cohort assessing nonmelanoma skin cancer as the outcome, 11,774 new users of GLP-1 RAs were compared with 207,788 new users of sulfonylureas. Cox proportional hazards models weighted using propensity score fine stratification were fit to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) of melanoma and nonmelanoma skin cancer, respectively. RESULTS: Compared with sulfonylureas, GLP-1 RAs were not associated with an increased risk of either melanoma (42.6 vs. 43.9 per 100,000 person-years, respectively; HR 0.96, 95% CI 0.53-1.75) or nonmelanoma skin cancer (243.9 vs. 229.9 per 100,000 person-years, respectively; HR 1.03, 95% CI 0.80-1.33). There was no evidence of an association between cumulative duration of use with either melanoma or nonmelanoma skin cancer. Consistent results were observed in secondary and sensitivity analyses. CONCLUSIONS: In this population-based cohort study, GLP-1 RAs were not associated with an increased risk of melanoma or nonmelanoma skin cancer, compared with sulfonylureas.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".