How a Canadian federal organization integrated synoptic reporting and quality improvement tools to drive a national learning health system in cancer surgery
Bibliographic record
Abstract
Accurate and complete surgical and pathology reports are the cornerstone of treatment decisions and cancer care excellence. Synoptic reporting is a process for reporting specific data elements in a specific format in surgical and pathology reports. Since 2007, the Canadian Partnership Against Cancer has led the implementation of synoptic reporting mechanisms across multiple cancer disease sites and jurisdictions across Canada. While the implementation of synoptic reporting has been successful, its use to drive improvements in the quality of cancer care delivery has been lacking. Here we describe the 4-year, national multi-jurisdictional quality improvement initiative to catalyse the use synoptic data to drive cancer system improvements. Resources provided to the jurisdictions included operational funding, training in quality improvement methodology, national forums, expert coaches, and ad hoc monitoring and support. The program emphasized foundational concepts including data literacy, audit and feedback reports, communities of practice, and positive deviance methodology.
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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 | 0.077 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.021 | 0.008 |
| Scholarly communication | 0.015 | 0.007 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".