Diabetes mellitus and cancer risk in a population‐based case–controlstudy among men from Montreal, Canada
Bibliographic record
Abstract
Diabetics may have a higher risk of cancer, notably liver and pancreatic cancers. Evidence about other cancer types remains sparse. The authors examined potential associations between diabetes and several types of cancer in a large multicancer case-control project carried out in Montreal, Canada, in the 1980s. This report, based on 3,107 male cancer cases and 509 population controls, uses information on diabetes and several covariates collected by interview. Adjusted odds ratios (ORs) and 95% confidence intervals (CI) were estimated for the associations between diabetes and each of 12 cancer types. Risks of pancreatic and liver cancers were increased among diabetics: adjusted ORs were 2.1 (95% CI: 1.0, 4.3) for pancreatic and 3.1 (95% CI: 1.1, 8.8) for liver cancer. The increased risk of pancreatic cancer was completely restricted to those with recent onset of diabetes; this was likely a manifestation of reverse causality. Conversely, the increased risk of liver cancer was independent of the interval between diabetes and cancer diagnoses. No associations were observed with melanoma, non-Hodgkin's lymphoma, cancers of the esophagus, stomach, colon, rectum, lung, prostate, bladder and kidney. In conclusion, diabetes was associated with an increased risk of liver cancer among men, but with no other cancer type including pancreatic 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".