Enabling quality data reporting: National implementation of standardization pathology reporting.
Bibliographic record
Abstract
122 Background: The Canadian Partnership Against Cancer (the Partnership) launched its Electronic Synoptic Pathology Reporting Initiative (ESPRI) to advance the implementation and promote the wide adoption of standardized synoptic pathology reporting tools in Canada. This presentation will highlight strategies to ensure successful implementation of this program across the Canadian health system as well as lessons learned. Methods: Upon consultation with clinical and system leaders, clear and measureable program targets (such as 90% completeness rates against mandatory elements of the College of American Pathology cancer protocols and disease-specific clinical indicators) were agreed upon. Thorough provincial planning to understand the variances across provincial current states, establish strategies and solidify plans was supported by the Partnership; followed by implementation of those plans. Key approaches to enable adoption and implementation include coordination of Pathologist and Vendor-led education sessions, establishment of clinical expert panels and assignment of Canadian representatives to the College of American Pathologists’ (CAP) Cancer Protocol Review Panels. The Partnership continues to collaborate with national/international standards organizations such as NAACCR, CAP, and Canada Health Infoway to support standards maintenance. Results: As a result of ESPRI, approximately 80% of Canadian pathologists will be reporting synoptically by 2017, across 7 of 10 provinces. It is expected that this will reflect approximately 25,000 reports per year for the five cancer types of focus (breast, colorectal, lung, prostate and endometrial) from about 950 pathologists. The Partnership, with CAP-ACP, has partnered with pathology communities from the US, UK, and Australasia to form the International Collaboration on Cancer Reporting (ICCR) to collaborate on internationally-harmonized core datasets for cancer pathology. Conclusions: Adoption of structured pathology reporting in Canada will enable better patient care, improve data quality, create efficiencies and enable interoperability. Through adoption and implementation efforts, this will impact patient 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 | 0.144 | 0.126 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.007 | 0.013 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".