Risk‐Adapted Starting Ages of Colorectal Cancer Screening for People With Diabetes or Metabolic Syndrome
Bibliographic record
Abstract
ABSTRACT Background Individuals with diabetes and metabolic syndrome have an increased risk of colorectal cancer (CRC), suggesting earlier screening than the average‐risk population may be warranted. Aims To derive risk‐adapted starting ages of CRC screening for people with diabetes or metabolic syndrome. Methods We determined 5‐year cumulative risks of CRC at individual ages between 30 and 50 across Europe (overall and individually for Germany, France, the UK and Italy) and North America (the United States and Canada) based on the GLOBOCAN 2022 database. Using risk estimates from meta‐analyses (2020–2023), we derived the ages at which individuals with diabetes or metabolic syndrome reach the same CRC risk as the average‐risk population at age 50 (aCR50) or 45 (aCR45). Results Individuals with diabetes were estimated to reach aCR50 at age 47 (95% confidence interval, 45–49) in Europe and 46 (42–49) in North America. For metabolic syndrome, the corresponding ages were 47 (47–48) in Europe and 46 (46–47) in North America. Disparities across countries were minimal, with deviations of up to no more than one year. For screening programmes starting at age 45, corresponding risk‐adapted starting ages for people with diabetes or metabolic syndrome were estimated to be 42 (41–44) and 43 (42–43) for Europe and 41 (38–44) and 41 (41–42) for North America, respectively. Conclusions People with diabetes or metabolic syndrome reach risk levels comparable to the average risk population three to four years earlier. Our results offer empirical guidance for defining risk‐adapted starting ages of CRC screening for these high‐risk groups.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.006 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".