Follow-up of Benign Thyroid Nodules—Can We Do Less?
Bibliographic record
Abstract
OBJECTIVE: To determine the incidence of malignancy, follow-up ultrasound (US), and repeat fine needle aspiration (FNA) in thyroid nodules that have been previously biopsied as benign. METHODS: This is a retrospective, descriptive study of benign thyroid nodules evaluated by US between 2010-2011. We determined the frequency of follow-up ultrasounds and FNAs, mean years of follow-up, interval between follow-up US, change in nodule size, reasons for repeat FNA (rFNA), frequency of thyroidectomy, and thyroid malignancy during 5 years of follow-up. RESULTS: A total of 733 benign thyroid nodules were reviewed in 615 patients. Mean years of US follow-up was 3.47 ± 1.65 years; 275 (37.5%) had no follow-up US; 109 (14.9%) had 1 follow-up US; 93 (12.7%) had 2 follow-up US; and 256 (34.9%) had 3 or more follow-up US. Assessment of thyroid nodule size showed that 215 (28.8%) nodules decreased in size, 145 (19.4%) increased in size by less than 50%, and 91 (12.1%) increased in size by more than 50%. Of the 733 nodules, 17 nodules (2.3%) underwent thyroidectomy for which the pathology result of 9 (1.2%) showed malignancy, and 65 (8.9%) thyroid nodules underwent rFNA. When applying the 2015 recommendations for repeat FNA, 35% were done unnecessarily. CONCLUSION: In our sample of initially benign thyroid nodules, only 9 patients (1.2%) had pathology-proven malignancy after a mean follow-up of 3.5 years. Over 30% of patients had more than 3 rUSs. Decreased interval and frequency of rUS should be considered in future guidelines for thyroid management.
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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 | 0.005 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".