The temporal relationship between diabetes and cancer: A population‐based study
Bibliographic record
Abstract
BACKGROUND: Diabetes is associated with an increased risk of several cancers; however, greater detection of cancer around the time of diabetes diagnosis may partly contribute to this relationship. The goal of the current study was to explore the temporal relationship between diabetes and cancer incidence. METHODS: The authors conducted a retrospective, population-based cohort study of >1 million adults living in Ontario, Canada to evaluate the association between diabetes diagnosis and the incidence of cancer in 3 time periods: within the 10 years before a diabetes diagnosis, within the first 3 months after a diabetes diagnosis, and from 3 months to 10 years after a diabetes diagnosis. RESULTS: Individuals with diabetes were significantly more likely to have been diagnosed with cancer within the 10 years before a diabetes diagnosis compared with individuals without diabetes (odds ratio, 1.23; 95% confidence interval [95% CI], 1.19-1.27). Cancer incidence also was found to be significantly higher in individuals with diabetes within the 3-month period after a diabetes diagnosis (hazard ratio, 1.62; 95% CI, 1.52-1.74), whereas the risk was not found to be elevated in the later period (hazard ratio, 0.97; 95% CI, 0.95-0.98). Similar trends were noted for individual cancers. CONCLUSIONS: The results demonstrated that individuals with diabetes had a significantly higher risk of most cancers, which was limited to the time periods before and immediately after a diabetes diagnosis. The highest risk period was observed within the first 3 months after a diabetes diagnosis, suggesting a partial role of detection bias in the apparent relationship between diabetes and cancer. Cancer 2016. © 2016 American Cancer Society. Cancer 2016;122:2731-2738. © 2016 American Cancer Society.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".