Incidence of Diabetes in Colorectal Cancer Survivors
Bibliographic record
Abstract
BACKGROUND: A higher risk of colorectal cancer (CRC) in patients with diabetes has been well documented. However, little is known regarding diabetes incidence in CRC survivors. This may have substantial impact on CRC survivorship care as well as enhancing the understanding of the interplay between the two diseases. We explored whether the incidence of diabetes was higher among patients with CRC than matched control subjects. METHODS: Using population-based data from Ontario, Canada, we generated a dataset comprising 39 707 incident CRC cases and 198 535 age- and sex-matched control subjects (1:5) dating from April 2002 to March 2010. We used cause-specific hazard models to estimate the hazard ratios (HRs) for diabetes overall and in subgroups stratified by receipt of systemic chemotherapy, diagnosis of metastatic disease, and site of cancer. RESULTS: During a mean follow-up of 4.81 years, the association between CRC and diabetes varied: The rate of developing diabetes was 53% higher among CRC patients compared with control subjects in the first year postdiagnosis (HR = 1.53, 95% confidence interval [CI] = 1.42 to 1.64) and remained increased by 19% in the fifth year postdiagnosis (HR = 1.19, 95% CI = 1.05 to 1.35). Findings were similar in subgroups of patients who had colon cancer, received systemic chemotherapy, or had no evidence of metastasis. CONCLUSION: We found that CRC patients were statistically significantly more likely to develop subsequent diabetes than persons without CRC for up to five years after the diagnosis. Our study suggests that active screening and counseling regarding modifiable risk factors may be warranted in this high-risk group.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".