A Pan-Canadian Prospective Study of Young Women with Breast Cancer: The Rationale and Protocol Design for the RUBY Study
Bibliographic record
Abstract
Introduction: The understanding of the biology and epidemiology of, and the optimal therapeutic strategies for, breast cancer (bca) in younger women is limited. We present the rationale, design, and initial recruitment of Reducing the Burden of Breast Cancer in Young Women (ruby), a unique national prospective cohort study designed to examine the diagnosis, treatment, quality of life, and outcomes from the time of diagnosis for young women with bca. Methods: Over a 4-year period at 33 sites across Canada, the ruby study will use a local and virtual recruitment model to enrol 1200 women with bca who are 40 years of age or younger at the time of diagnosis, before initiation of any treatment. At a minimum, comprehensive patient, tumour, and treatment data will be collected to evaluate recurrence and survival. Patients may opt to complete patient-reported questionnaires, to provide blood and tumour samples, and to be contacted for future research, forming the core dataset from which 4 subprojects evaluating genetics, lifestyle factors, fertility, and local management or delivery of care will be performed. Summary: The ruby study will be the most comprehensive repository of data, biospecimens, and patient-reported outcomes ever collected with respect to young women with bca from the time of diagnosis, enabling research unique to that population now and into the future. This research model could be used for other oncology settings in Canada.
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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 | 0.082 | 0.054 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".