The Use of Metformin and Colorectal Cancer Incidence in Patients with Type II Diabetes Mellitus
Bibliographic record
Abstract
BACKGROUND: Experimental studies have suggested that metformin may decrease the incidence of colorectal cancer in patients with type II diabetes. However, previous observational studies have reported contradictory results, which are likely due to important methodologic limitations. Thus, the objective of this study was to assess whether the use of metformin is associated with the incidence of colorectal cancer in patients with type II diabetes. METHODS: A cohort study of patients newly treated with non-insulin antidiabetic agents was assembled using the United Kingdom Clinical Practice Research Datalink. A nested case-control analysis was conducted, where all incident cases of colorectal cancer occurring during follow-up were identified and randomly matched with up to 10 controls. Conditional logistic regression was used to estimate adjusted rate ratios (RR) of colorectal cancer associated with ever use, and cumulative duration of use of metformin. All models accounted for latency and were adjusted for relevant potential confounding factors. RESULTS: Overall, ever use of metformin was not associated with the incidence of colorectal cancer [RR: 0.93; 95% confidence interval (CI), 0.73-1.18]. Similarly, no dose-response relationship was observed in terms of cumulative duration of use. CONCLUSIONS: The use of metformin was not associated with the incidence of colorectal cancer in patients with type II diabetes. IMPACT: The results of this study do not support the launch of metformin randomized controlled trials for the chemoprevention of colorectal 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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".