Retrospective Application of the 2015 American Thyroid Association Guidelines for Ultrasound Classification, Biopsy Indications, and Follow-up Imaging of Thyroid Nodules: Can Improved Reporting Decrease Testing?
Bibliographic record
Abstract
INTRODUCTION: Thyroid ultrasound has been widely used to determine which nodules need further investigation. The goal of this study is to determine if using an ultrasonographic features checklist based on 2015 American Thyroid Association (ATA) guidelines can improve reporting and decrease unnecessary further testing. METHODS: In this retrospective study, ultrasonographic images of all nodules biopsied at our institution in 2014 and 2015 were reviewed by radiologists blinded to fine needle aspiration (FNA) biopsy result using a checklist. The checklist was prepared based on 2015 ATA guidelines. The ultrasonographic characteristics of thyroid nodules were compared with the result of biopsy to determine positive predictive value (PPV), negative predictive value (NPV), sensitivity, and specificity for predicting malignancy. Radiologists also made an overall recommendation on need for FNA. RESULTS: A total of 425 thyroid nodule ultrasound scans were reviewed by radiologists. Biopsy results of 31 nodules were malignant and 394 were non-malignant. Malignant nodules showed higher frequency of solid composition, hypoechoechogenicity, and cervical lymph node involvement compared to benign nodules. Solid nodule composition had the highest PPV (13%) and NPV (94.7%). Extra-thyroid extension had the highest specificity (90.1%). Lesion vascularity had the highest sensitivity (83.8%), followed by hypoechogenicity (65.6%). Overall, the checklist had a positive predictive value of 9%, negative predictive value of 97.5%, sensitivity of 96.8%, and specificity of 11.14%. Radiologists determined that 10% of the nodules were very low-risk and did not require FNA. CONCLUSION: Using a checklist based on 2015 ATA guideline thyroid nodule ultrasonographic features is a sensitive tool with high NPV to predict benign thyroid nodule, thereby preventing unnecessary FNAs.
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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 | 0.028 | 0.090 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".