Adolescent and young adult (<scp>AYA</scp>) oncology: A credentialed area of focused competence in Canada
Bibliographic record
Abstract
BACKGROUND: Adolescents and young adults (AYA, ages 15-39 years) affected by cancer have unique treatment, survivorship, and palliation concerns. Current oncology training does not focus on the distinctive needs of this demographic. Amid this recognition, the Canadian National AYA Cancer Task Force and Canadian Partnership Against Cancer have advocated the need for clinicians with formalized AYA experience. To address this need and standardize training, a national task force developed criteria for structured academic programs in AYA Oncology in Canada. METHODS: Workshops were organized to identify and establish the fundamentals of practice in AYA Oncology through consensus. These workshops followed the pre-existing rigorous process established by the Royal College of Physicians and Surgeons of Canada (Royal College) for new program development. The process includes: (i) developing the tasks associated with the discipline's practice, (ii) identifying the evidence trainees must provide to demonstrate tasks can be performed independently (the competence portfolio), (iii) developing training requirements and summarizing the knowledge, skills and attitudes required to perform these tasks, and (iv) identifying specific experiences essential to acquiring skills and demonstrating competent performance. RESULTS: AYA Oncology is a recognized an Area of Focused Competence (AFC) by the Royal College. CONCLUSION: The AFC designation in AYA Oncology provides a standardized curriculum, training experience and accreditation process to attract oncologists, promote expertise and advance AYA oncology care.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".