Early-Age-Onset Colorectal Cancer in Canada: Evidence, Issues and Calls to Action
Bibliographic record
Abstract
The inaugural Early-Age-Onset Colorectal Cancer Symposium was convened in June 2021 to discuss the implications of rapidly rising rates of early-age-onset colorectal cancer (EAO-CRC) in Canadians under the age of 50 and the impactful outcomes associated with this disease. While the incidence of CRC is declining in people over the age of 50 in Canada and other developed countries worldwide, it is significantly rising in younger people. Canadians born after 1980 are 2 to 2.5 times more likely to be diagnosed with CRC before the age of 50 than previous generations at the same age. While the etiology of EAO-CRC is largely unknown, its characteristics differ in many key ways from CRC diagnosed in older people and warrant a specific approach to risk factor identification, early detection and treatment. Participants of the symposium offered directions for research and clinical practice, and developed actionable recommendations to address the unique needs of these individuals diagnosed with EAO-CRC. Calls for action emerging from the symposium included: increased awareness of EAO-CRC among public and primary care practitioners; promotion of early detection programs in younger populations; and the continuation of research to identify unique risk factor profiles, tumour characteristics and treatment models that can inform tailored approaches to the management of EAO-CRC.
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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 | 0.044 | 0.117 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".