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 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 | 0.022 | 0.093 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".