Breaking Down Barriers to Detection and Care in Early-Age-Onset Colorectal Cancer in Canada
Bibliographic record
Abstract
The second Early-Age-Onset Colorectal Cancer Symposium, convened in October 2022, sought solutions to the barriers to early detection and care for colorectal cancer in Canada. This meeting built on a previous symposium, held in 2021 and reported in this journal. Early-age-onset colorectal cancer (EAOCRC) affects increasing numbers of people under the age of 50 in Canada and throughout the developed world. Two main themes emerged from the meeting: the importance of timely detection, and the need for a tailored approach to the care of EAOCRC. Early detection is crucial, especially in light of the later stage at diagnosis and unique tumour characteristics. Symposium participants were strongly in favour of reducing the age of eligibility for screening from 50 to 45, and promoting the development of non-invasive screening techniques such as testing for circulating tumour DNA and biomarkers. Leading approaches to care were described and discussed, which meet the unique treatment needs of younger CRC patients. Multidisciplinary practices within and outside Canada address such factors as fertility, family roles, education, careers and financial responsibilities. These models can be applied in treatment centres across the country.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".