A CONTOUR BY ANY OTHER NAME: PROVINCIAL LOGISTICS OF IMPLEMENTING THE PAN-CANADIAN DATA STRATEGY
Bibliographic record
Abstract
Standardizing oncology data is essential for improving patient care, optimizing treatment pathways, and advancing research. Since 2018, the American Association of Physicists in Medicine (AAPM) has led efforts to develop TG-263 (radiotherapy nomenclature) and the Operational Ontology for Oncology (O3). However, aligning these standards with clinical practice remains a challenge. The Pan-Canadian Cancer Data Strategy, launched by the Canadian Partnership Against Cancer (CPAC) and the Canadian Cancer Society (CCS), has identified radiotherapy (RT) as a key focus for structured oncology data implementation. Spearheaded by the provinces of Ontario (ON) and Nova Scotia (NS), this initiative seeks to standardize and integrate oncology data, ultimately improving patient outcomes and supporting evidence-based decision-making. An early-adopter initiative was launched in ON and NS, engaging 15 RT programs in ON and 2 RT sites in NS to evaluate the feasibility of TG-263 and O3 adoption. Key focus areas included: • Standardizing nomenclature for structured RT data capture • Exploring tools for standard nomenclature and contour naming • Evaluation of tools for compliance auditing, structure naming corrections, and enhancing automated data validation and processing • Assessing the feasibility of O3 implementation across centres with diverse infrastructure and workflows A current state evaluation examined data standardization practices, infrastructure and feasibility across participating centres. Challenges and opportunities in establishing a pan-Canadian RT data standard include: • Challenges: • mobilizing resources, whether human or financial, to promote adoption • partner buy-in is crucial, within radiation treatment programs and among clinicians, recognizing the importance of standard nomenclature • evaluating the pros and cons of the numerous vendor and open-source compliance auditing tools available and sustainability considerations. • interoperability of tools with various radiation therapy software systems particular within a mixed vendor environment • Opportunities: • Evaluate the feasibility and scalability of the initiatives, including TG-263 and O3 adoption • share lessons learned to provide support and further collaboration between the two provinces and at a pan-Canadian level as other programs pursue this initiative Findings from the early-adopter programs will provide blueprint for a broader pan-Canadian rollout, including the validation of tools for nomenclature and auditing to guide data linkage improvements. These lessons learned will serve as a foundation for standardizing and integrating oncology data, ultimately enhancing patient outcomes and supporting evidence-based decision-making.
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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 | 0.054 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.019 | 0.005 |
| Scholarly communication | 0.016 | 0.007 |
| Open science | 0.006 | 0.012 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.027 | 0.006 |
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".