Tips for improving consistency of thyroid nodule interpretation with ACR TI-RADS
Bibliographic record
Abstract
Thyroid nodules are very common in the general population. Most are benign and even those that are malignant are typically slow-growing and do not require treatment. Overdiagnosis and overtreatment of thyroid nodules has resulted in significant healthcare costs. ACR TI-RADS was developed to address these concerns, and reduce the number of unnecessary biopsies and follow-up intervals. ACR TI-RADS offers a point-based risk stratification system centered on five sonographic features: consistency, echogenicity, shape, margins and echogenic foci. While the system has noticeable benefits and comparable accuracy with other available risk stratification systems (ATA, EU-TIRADS and K-TIRADS), there are inherent challenges relating to suboptimal inter-reader agreement. In this article, we include 10 educational tips that may be helpful to the ultrasound practitioner for improving the consistency of nodule interpretation with ACR TI-RADS.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".