Examining the Impact of First Nations Status on the Relationship Between Diabetes and Cancer
Bibliographic record
Abstract
Purpose: This population-based study examined the relationship between diabetes and cancer and determined if this relationship was influenced by First Nations (FN) status. Methods: In a matched case–cohort study, individuals 30–74 years of age diagnosed with diabetes during 1984–2008 in the province of Manitoba, Canada, with no cancer diagnosis before their diabetes diagnosis were matched to one diabetes-free control by age, sex, FN status, and residence. Flexible competing risk and Royston–Parmar regression models were used to compare cancer rates. Results: Overall, 72,715 individuals diagnosed with diabetes were matched to controls. In all age groups, diabetes was related to an increased risk of cancer. The relationship between diabetes and any type of cancer was not influenced by FN status (i.e., there was no interaction between the diagnosis of diabetes and people's FN status for any age group). The only significant interaction between diabetes and FN status was for kidney cancer for individuals 60–74 years of age; diabetes increased the risk of kidney cancer for all other Manitobans (AOMs) but not for FN. Conclusions: Diabetes increased the risk of cancer. The association was not modified by FN status except for kidney cancer where diabetes increased the risk for AOMs but not for FN.
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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 | 0.000 |
| 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".