The cancer imaging program quality framework at Cancer Care Ontario: The first five years.
Bibliographic record
Abstract
244 Background: The Cancer Imaging Program (CIP) at Cancer Care Ontario was established in 2009 to improve the quality of cancer imaging in Ontario. Methods: After initial selection of a Provincial clinical lead in 2009, fourteen regional clinical leads were selected to represent all geographical regions of the province. Through a stakeholder survey and a priority setting process the following four high-level areas of priority emerged to support quality improvement of cancer imaging: (1) Developing and Fostering an Imaging Community of Practice, (2) Imaging Appropriateness, (3) Timely Access to Imaging, and (4) Standardized/Synoptic Reporting. Results: (1) An Imaging Community of Practice was established with the regional clinical leads, who participate in monthly meetings to build and strengthen inter-regional relationships and share information on regional activities and priorities; (2) Best practice standards for imaging in lung and colorectal cancer have been developed by consolidating and endorsing national and international guidelines. New imaging guidelines are being developed by the Program in Evidence-Based Care. Evidence-based recommendations being developed for focal tumour ablation procedures; (3) Three Interventional Radiology procedures (CT-guided lung biopsies, peripherally inserted central catheters and portacaths) have been selected for an ongoing wait time collection that captures monthly point-in-time data. The data has initiated discussions on appropriate benchmarks and identification of factors that may contribute wait times; and (4) Synoptic Radiology Reporting enables the collection of uniform and complete data to improve the information available to referring clinicians for diagnosis and treatment planning. Work is underway in the development of: implementation roadmap, evidence-based clinical checklists, infrastructure to store and share synoptic reports, and international standards for synoptic radiology reporting. Conclusions: The establishment of the CIP as a clinical program under a provincial cancer agency has enabled the development of an Imaging Community of Practice and allowed for work on provincial-wide initiatives that enable quality improvement of cancer imaging.
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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 | 0.043 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".