The Ovarian/Adnexal Reporting and Data System for Ultrasound: From Standardized Terminology to Optimal Risk Assessment and Management
Bibliographic record
Abstract
The American College of Radiology (ACR) Ovarian-Adnexal Reporting and Data System (O-RADS) lexicon and risk assessment tool for ultrasound (US) provides a framework for characterization of ovarian and adnexal pathology with the ultimate goal of harmonizing reporting and patient management strategies. Since the first O-RADS US publication in 2018, multiple validation studies have shown O-RADS US to have excellent diagnostic accuracy, with the majority of these studies using O-RADS 4 as the optimal cut-off for detecting ovarian cancer. Most of the existing validation studies include a dedicated training phase and confirm that ORADS US categories and lexicon descriptors are associated with high level inter-read agreement, regardless of radiologist training level or practice experience. O-RADS US has a similar inter-reader agreement when compared to Gynecologic Imaging Reporting and Data System (GIRADS), Assessment of Different Neoplasias in the adnexa (ADNEX), and International Tumor Analysis Group (IOTA) simple rules. System descriptors have been shown to correlate with expected malignancy rates and the O-RADS US risk stratification system has been shown to perform in the expected range of malignancy risk per category. Further directions will focus on clarifying governing concepts and lexicon terminology as well as further refining risk stratification categories based on data from published validation studies.
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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 | 0.051 | 0.102 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".