Metformin and the incidence of viral associated cancers in patients with type 2 diabetes
Bibliographic record
Abstract
Limited studies have associated metformin with a reduced risk of viral associated cancers, however these had a number of methodological shortcomings. This study investigated whether the use of metformin is associated with a reduced risk of viral associated cancers in patients with type 2 diabetes. A cohort of 137,754 patients newly-prescribed non-insulin antidiabetic drugs between January 1, 1988 and March 31, 2016 was identified from the UK Clinical Practice Research Datalink and followed until a first-ever diagnosis of a viral associated cancer, death from any cause, end of registration with the practice, or March 31, 2016. Time-varying use of metformin was compared with use of other antidiabetic drugs, with exposures lagged by one year for latency purposes. Time-dependent Cox proportional hazards models were used to estimate adjusted hazard ratios (HRs) with 95% confidence intervals (CIs) of incident viral associated cancer with use of metformin overall, by cumulative duration of use and viral etiology. Overall, there were 424 viral associated cancers during 759,810 person-years of follow-up (crude rate of 5.6 per 10,000 person-years). Metformin was not associated with a decreased rate of viral associated cancer (HR: 0.93, 95% CI: 0.65-1.32). There was no evidence of a duration-response relationship in terms of cumulative duration of use (p trend = 0.69), including with use of metformin for more than 10 years (HR 1.02, 95% CI: 0.52-1.99), or by viral etiology. In this large population-based cohort study, the use of metformin was not associated with a reduced risk of viral associated cancer.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".