Application of the new classification criteria in ovarian pathology (IOTA – O-RADS)
Bibliographic record
Abstract
The Ovarian-Adnexal Reporting and Data System (O-RADS) risk stratification and management system is designed to provide consistent interpretations, to decrease or eliminate ambiguity in ultrasound (US) reports, resulting in a higher accuracy in assigning the risk of malignancy for ovarian and other adnexal masses, and to provide accurate management recommendations for each risk category. It was developed by experts in radiology, gynecology, pathology and gynecologic oncology from the United States, Canada and Europe, under the supervision of the ACR (American College of Radiology). This unique system represents a collaboration between the pattern-based approach, commonly used in North America, and the widely used, European-based, algorithmic-style of the International Ovarian Tumor Analysis (IOTA) Assessment of Different Neoplasias in the Adnexa model system, a risk prediction model that has undergone successful prospective and external validation. The Ovarian-Adnexal Reporting and Data System US risk stratification and management system for evaluation of ovarian and other adnexal masses is based on a standardized lexicon, incorporates all classes of risk, and offers an associated management strategy for each risk category. US is widely considered the primary imaging modality in the evaluation of women with suspected adnexal pathology. Standardized terms and definitions to describe the sonographic features of adnexal lesions have been proposed, and several US reporting models can aid in differentiating benign from malignant adnexal masses. However, approximately 20% to 25% of adnexal masses remain indeterminate after the initial sonographic evaluation. Furthermore, studies have demonstrated variable positive predictive values for the detection of ovarian cancer using US, with certain studies showing low positive predictive values in the general population in which the incidence of ovarian cancer is low. Secondary tests such as MRI could help decrease the number of false-positive lesions when using US in certain settings and avoid unnecessary surgery in benign lesions. The ACR Ovarian-Adnexal Reporting and Data Systems (O-RADS) MRI Committee has developed an evidence-based lexicon and risk stratification system for MRI evaluation of adnexal lesions.
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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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".