Treatment patterns and survival outcomes of early-onset colorectal cancer patients in Alberta, Canada: a population-based study
Bibliographic record
Abstract
BACKGROUND: The incidence of early-onset (<50) colorectal cancer (eoCRC) has been increasing in Canada. Little is known about treatment patterns and outcomes among this patient population in Canada. PATIENTS AND METHODS: We conducted a retrospective population-based cohort study of CRC patients in Alberta (2010-2018) using electronic medical records and administrative claims data. Treatment patterns and CRC-specific mortality were compared between early-onset age groups (<40 and 40-49) and average age-at-onset (60-70) (aoCRC) patients with multivariable logistic regression and cox proportional hazard models. RESULTS: There were 334 and 935 patients in the early-onset groups and 4606 in the aoCRC group. Compared with aoCRC, patients <40 were more likely to receive chemotherapy in stage II colon (OR 3.41, CI 1.75-6.47) and stage III rectal (OR 3.01, CI 1.18-10.21), and to receive systemic therapy (OR 2.40, CI 1.46-4.12) and radiation in stage IV CRC (OR 2.70, CI 1.48-4.92). The 40-49 age group was more likely to receive chemotherapy in stage II colon (OR 2.13, CI 1.25-3.56), and chemoradiation in stage II rectal (OR 2.16, CI 1.25-3.80) and stage III rectal (OR 1.63, CI 1.13-2.40), as well as systemic therapy in stage IV CRC (OR 2.46, CI 1.75-3.52). Survival did not differ between <40 and 60-70 age groups. Survival was significantly higher for the 40-49 age group, but only in stage IV (HR 0.79, CI 0.67-0.94). CONCLUSIONS: EoCRC patients tended to receive more therapy than average age CRC patients with minimal survival gains. Additional research to identify optimal treatment strategies for eoCRC patients is required.
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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 | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".