Diabetes medications and pancreatic cancer risk: A population-based cohort study
Bibliographic record
Abstract
BACKGROUND: Studies of the relationship between diabetes medications and pancreatic cancer risk have produced inconclusive results. We aimed to examine associations between classes, subclasses, and individual diabetes medications with pancreatic cancer risk in a population-based retrospective cohort study. METHODS: Among British Columbians aged ≥ 35 (1996-2019), prescriptions for diabetes medications were categorised by ever/never use, cumulative duration, and dose. Time-varying Cox proportional hazards models adjusted for demographics were used to estimate hazard ratios (HRs) and 95 % confidence intervals (CIs) for associations between new diabetes medication use and pancreatic cancer. Confounding by indication was explored using active comparator analysis of ever/never associations relative to pioglitazone use. RESULTS: The cohort consisted of 3,118,538 people (52,088,644 person-years), 7,540 of whom were diagnosed with pancreatic cancer. For every one-year increase in cumulative dose, diabetes medications in the insulin secretagogue class, and glyburide; an individual medication within the class, were associated with 2 % (HR=1.02, 95 % CI=1.02-1.03) and 3 % (HR=1.03, 95 % CI=1.02-1.05) increased risk of pancreatic cancer. For every one-year increase in cumulative dose, medications within the insulins and analogues class and insulin subclasses (basal and bolus insulins) were linked to a 4 % higher risk (HR=1.04, 95 % CI=1.03-1.05) of pancreatic cancer. In the active comparator analysis, elevated risk for basal insulins (HR=1.49, 95 % CI=0.33-6.63) was observed, consistent with the main analysis, although the risk was not statistically significant. CONCLUSION: Basal insulins may be associated with higher pancreatic cancer risk. Although confirmatory studies are needed, this finding may be informative for prescribing practices for high-risk populations with diabetes.
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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 | 0.002 | 0.004 |
| 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".