Implementation of O-RADS Ultrasound Reporting System: A Quality Improvement Initiative
Bibliographic record
Abstract
Objectives: To determine the feasibility of implementing Ovarian-Adnexal Reporting & Data System (O-RADS) ultrasound (US) for reporting of adnexal masses at our institution, with a specific goal of increasing the use of O-RADS from a baseline of <5% to at least 75% over a 16-month period. Methods: A prospective interrupted time series quality improvement study was undertaken over a 16-month period. Plan, do, study, act cycles included: (1) Engagement of interested parties, (2) Targeted educational sessions, (3) Development of reporting templates, (4) Weekly audit-feedback. Inter-reader variability assessment was performed on 70% of O-RADS risk-category 2 to 5. The primary outcome was the reporting of an O-RADS risk category. Results: A total of 635 female pelvic US were performed at our centre between July 2022 and April 2023. An O-RADS risk category was provided on the final radiology report by the radiologist for 489/635 (77%) US. From November 2022 to April 2023, the weekly rate of O-RADS risk category reporting reached 88%. The O-RADS score was concordant between readers for 83/103 (81%) of US reports with kappa score of 0.69 corresponding to good agreement. Conclusions: The reporting of O-RADS risk category increased from <5% to 88% over a 16-month period with a high level of agreement among readers in assigning O-RADS risk category. Implementation of a standardizing reporting ultrasound system at a tertiary cancer centre is feasible with rapid learning and uptake curves.
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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 | 0.103 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".