Reliability of ultrasound ovarian-adnexal reporting and data system amongst less experienced readers before and after training
Bibliographic record
Abstract
BACKGROUND: The 2018 ovarian-adnexal reporting and data system (O-RADS) guidelines are aimed at providing a system for consistent reports and risk stratification for ovarian lesions found on ultrasound. It provides key characteristics and findings for lesions, a lexicon of descriptors to communicate findings, and risk characterization and associated follow-up recommendation guidelines. However, the O-RADS guidelines have not been validated in North American institutions or amongst less experienced readers. AIM: To evaluate the diagnostic accuracy and inter-reader reliability of ultrasound O-RADS risk stratification amongst less experienced readers in a North American institution with and without pre-test training. METHODS: < 0.05. RESULTS: < 0.001). Nineteen of 22 (86%) misclassified cases in pre-training were related to mischaracterization of dermoid features or wall/septation morphology. Fifteen of 17 (88%) of post-training misclassified cases were related to one of these two errors. Fleiss kappa inter-reader reliability was 'good' and pairwise inter-reader reliability was 'very good' with pre-training and post-training assessment (k = 0.76 and 0.77; and k = 0.77-0.87 and 0.85-0.89, respectively). CONCLUSION: irregular inner wall/septation morphology may improve sensitivity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".