Patterns of Colorectal Cancer Diagnosis Among Younger Adults in a Real-World, Population-Based Cohort
Bibliographic record
Abstract
Aims: To review the patterns of early-onset (<50 years old) colorectal cancer (CRC) in Alberta across the past 15 years among different socioeconomic and demographic patient subgroups. Methods: This is a retrospective, population-based study based on Alberta administrative databases. Income level was identified via income information from the 2006 Canadian census. Patients with colorectal adenocarcinoma diagnosed 2004–2018 were included. Frequency analyses were used to examine the percentage of early-onset CRC cases among different subgroups over the period studied. Multivariable logistic regression analysis was used to examine factors associated with the development of early-onset CRC. Results: A total of 24,912 patients were included, of whom 2096 (8.4%) were diagnosed at age <50 years and 22,816 (91.6%) at age ≥50 years. The percentage of patients diagnosed at age <50 years increased over time (10.2% in 2018 vs 7.9% in 2004; p < 0.003). Higher income was associated with younger age at diagnosis of CRC (odds ratio [OR] for quartile 1 vs quartile 4: 0.54; 95% CI: 0.47–0.62). Other factors associated with younger age at diagnosis included female sex (OR for male vs female: 0.85; 95% CI: 0.78–0.94), distal CRC (OR: 1.66; 95% CI: 1.50–1.84) and North zone (OR for South zone vs North zone: 0.74; 95% CI: 0.60–0.92). Conclusion: The proportion of patients (out of the overall CRC population) with early-onset CRC, increased in Alberta throughout the study duration (particularly left-sided CRC). There is a need to reassess the current age limits for CRC screening in Canada in view of these findings.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".