Standardizing architecture and governance of radiology clinical checklist development.
Bibliographic record
Abstract
193 Background: To improve quality of radiology reporting, Cancer Care Ontario’s (CCO) Cancer Imaging Program established synoptic radiology reporting as a priority area. Program goals are to implement standardized radiology reports across the province, improving communication between referring and interpreting physicians and providing a standardized foundation for staging data collection and population health research. Although there are libraries of structured radiology clinical checklists, development methodologies quality vary. To support ongoing development and provide a framework to assess existing checklists, the program developed and published two white papers. Methods: To ensure checklists are consistent in format, the first white paper provides guidance on ‘architecture’ (high-level elements) of a cancer imaging report. To ensure the content of adopted checklists are based on high-quality evidence, the second white paper focuses on clinical checklist development governance. Both white papers were developed in consultation with multidisciplinary expert panels assembled by CCO and underwent peer review prior to being made available. Results: The architecture white paper outlines the minimum mandatory elements for cancer imaging reports. The elements to be included in these reports are: demographics, relevant clinical information, body of the report, and impression. This paper provides specific guidance for expert panels in the development of new clinical checklists as well as criteria for reviewing existing checklists. The governance white paper provides a clear methodology for a systematic approach to clinical checklist development for synoptic radiology. Included are recommendations on the constitution of the clinical expert panels, the level of evidence needed to support checklist items, external review of the checklist, and periodic checklist maintenance. Conclusions: CCO has developed two white papers that serve as a guide for both CCO and external parties in the creation of high-quality clinical checklists. Improved standardization of the structure and development approach for clinical checklists facilitates both in-house development and adoption of third party checklists.
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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 | 0.512 | 0.615 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.017 | 0.011 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.021 | 0.013 |
| Open science | 0.010 | 0.019 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".