Incretin‐based drugs and risk of lung cancer among individuals with type 2 diabetes
Bibliographic record
Abstract
AIM: To assess whether dipeptidyl peptidase-4 inhibitors and glucagon-like peptide-1 receptor agonists are associated with an increased lung cancer risk among individuals with type 2 diabetes. METHODS: We conducted a population-based cohort study using the UK Clinical Practice Research Datalink. We identified 130 340 individuals newly treated with antidiabetes drugs between January 2007 and March 2017, with follow-up until March 2018. We used a time-varying approach to model use of dipeptidyl peptidase-4 inhibitors and glucagon-like peptide-1 receptor agonists compared with use of other second- or third-line antidiabetes drugs. We used Cox proportional hazards models to estimate the adjusted hazard ratios, with 95% CIs, of incident lung cancer associated with use of dipeptidyl peptidase-4 inhibitors and glucagon-like peptide-1 receptor agonists, separately, by cumulative duration of use, and by time since initiation. RESULTS: A total of 790 individuals were newly diagnosed with lung cancer (median follow-up 4.6 years, incidence rate 1.5/1000 person-years, 95% CI 1.4-1.6). Compared with use of second-/third-line drugs, use of dipeptidyl peptidase-4 inhibitors and glucagon-like peptide-1 receptor agonists was not associated with an increased lung cancer risk (hazard ratio 1.07, 95% CI 0.87-1.32, and hazard ratio 1.02, 95% CI 0.68-1.54, respectively). There was no evidence of duration-response relationships. CONCLUSIONS: In individuals with type 2 diabetes, use of incretin-based drugs was not associated with increased lung cancer risk.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".