The utility of thyroid ultrasonography in the management of thyroid nodules
Bibliographic record
Abstract
BACKGROUND: Ultrasonography for thyroid nodules is one of the most common imaging tests performed in the general population. Details from ultrasound reports guide biopsies and surgery. This study quantifies the completeness of these reports based on Thyroid Imaging and Reporting System (TI-RADS) criteria and considers their utility in predicting malignant disease. METHODS: We retrospectively reviewed ultrasound reports for 329 thyroidectomy patients and extracted data elements using the TI-RADS criteria: nodule size, echogenicity, margins, vascularity, solid/cystic composition and the presence or absence of microcalcifications and the halo sign. We assessed the reports to determine whether individual or multiple criteria were associated with malignancy. RESULTS: More than 97% of reports document nodule size; however, more than 90% of the reports noted only 3 or fewer of the 6 remaining TI-RADS criteria. The presence of microcalcifications was the most sensitive marker of malignancy (> 90%), whereas the documentation of irregular margins was the most specific indicator of malignancy (88%). Overall it was clear that microcalcifications, hypoechogenicity, irregular margins and solid nodules were significantly more likely to be found in malignant neoplasms; their absence predicted benign disease. Because so few reports consistently documented all criteria, the overall ability of thyroid ultrasonography to discriminate between lowerand higher-risk nodules is limited. CONCLUSION: Although the accuracy of thyroid ultrasonography is good, few ultrasound reports contain the necessary information, as defined by TI-RADS, to predict malignancy and guide management. When reported, microcalcifications and/or irregular margins are the best predictors of malignancy.
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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 | 0.012 | 0.076 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".