Is Ultrasonography Useful in Predicting Thyroid Cancer in Children with Thyroid Nodules and Apparently Benign Cytopathologic Features?
Bibliographic record
Abstract
BACKGROUND/AIMS: To assess whether the presence of certain findings on thyroid ultrasonography (US) correctly diagnoses malignancy even when a fine-needle aspiration biopsy (FNAB) suggests a benign lesion. METHODS: We reviewed the charts of 35 children and adolescents with a thyroid nodule who had had an US and a FNAB, and for whom final pathology was available. RESULTS: The global accuracy of FNAB was 83%, with a sensitivity of 75% and a specificity of 94%. 14 FNABs suggested malignancy (40%), only 1 of which was a false positive (7%). By contrast, 5 of the 21 FNABs suggesting benign lesions were false negatives (24%). These 5 cases had US findings suggestive of malignancy. When FNAB suggested a benign lesion, US had a good sensitivity (80%) but a poor specificity and accuracy (50 and 57%, respectively); its negative predictive value was 90% and its positive predictive value 36%. CONCLUSIONS: US complements FNAB in the evaluation of thyroid nodules in children. A more aggressive approach is warranted in children with a thyroid nodule and a benign FNAB if US findings suggest 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 | 0.002 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".