Development and implementation of communities of practice at Cancer Care Ontario (CCO).
Bibliographic record
Abstract
58 Background: A “Community of Practice” (CoP) has been defined as a group of people who share a passion for something they do and learn to improve as they translate knowledge and interact together. Methods: The primary goal of the Radiation Treatment Program (RTP) at CCO is to improve the quality of care delivered to Ontario patients receiving radiation treatment. We identified variations in practice in the 14 Regional Cancer Centers (RCC) in Ontario and facilitated the development of 3 CoPs to share best practices and standardize care where appropriate. Through this initiative, two multidisciplinary [Radiation Oncologists, Physicists and Radiation Therapists (MRT(T)s)] CoPs have been established in Head and Neck (H&N) and Gynecological (GYN) Cancers and one discipline-specific CoP has been started in Radiation Treatment External Beam Delivery involving MRT(T)s alone . During initial meetings of each CoP key variations in practice affecting quality of care were identified. Results: Each CoP has developed recommendation documents for improving care: H&N Cancer – Nomenclature for anatomic structures in treatment volumes and Evaluation of Intensity Modulated treatment Plans; GYN Cancer – Imaging strategies for Intracavitary Brachytherapy of Cervical Cancer; Treatment Delivery – Protocol Development Toolkit and Image Guided Radiation Therapy Education Checklist. These recommendation documents are currently being piloted in each RCC with a view to standardizing care across the province. Conclusions: These CoP initiatives have enabled the development of recommendation reports by front-line staff to ensure evidence-based, high-quality radiation treatment to improve the safety and quality of care for all cancer patients across a jurisdiction of 13M people.
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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 | 0.065 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.014 | 0.005 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.006 | 0.017 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".