Survival Tree Analysis of Interactions Among Factors Associated With Colorectal Cancer Risk in Patients With Type 2 Diabetes: Retrospective Cohort Study
Bibliographic record
Abstract
Background: Colorectal cancer (CRC) and diabetes share many common lifestyle risk factors, such as obesity. However, it remains largely unknown how different factors interact to influence the risk of CRC development among patients with diabetes. Objective: This study aimed to identify the interaction patterns among factors associated with the risk of CRC incidence among patients with diabetes. Methods: This is a retrospective cohort study conducted using electronic health records from Hong Kong. Patients who were diagnosed with type 2 diabetes and received care in general outpatient clinics between 2010 and 2019 without cancer history were included and followed up until December 2019. A conditional inference survival tree was applied to examine the interaction patterns among factors associated with the risk of CRC. Results: A total of 386,325 patients were included. During a median follow-up of 6.2 years (IQR 3.3-8.0), 4199 patients developed CRC. Patients were first partitioned into 4 age groups by increased levels of CRC risk (≤54 vs 55 to 61 vs 62 to 73 vs >73 years). Among patients aged more than 54 years, male sex was the dominant risk factor for CRC within each age stratum and the associations lessened with age. Abdominal obesity (waist-to-hip ratio >0.95) and longer duration of diabetes (median 12, IQR 7-18 vs median 4, IQR 1-11 years) were identified as key risk factor for CRC among men aged between 62 and 73 years and women aged more than 73 years, respectively. Conclusions: This study suggests the interaction patterns among age, sex, waist-to-hip ratio, and duration of diabetes on the risk of CRC incidence among patients with diabetes. Findings of the study may help identify target groups for public health intervention strategies.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".