Pediatric thyroid nodules: ultrasonographic characteristics and inter-observer variability in prediction of malignancy
Bibliographic record
Abstract
BACKGROUND: Pediatric thyroid nodules, while uncommon, have high malignancy risk. The objectives of the study were (1) to identify sonographic features predictive of malignancy; (2) to create a prediction model; and (3) to assess inter-observer agreement among radiologists. METHODS: All available cases of thyroid nodules, surgically removed between 2000 and 2009. Three radiologists reviewed the sonographic images; 2 pathologists reviewed the tissue specimens. Adult prediction models were applied. Interobserver variability was assessed. RESULTS: Twenty-seven subjects, mean age 13.1±3.4 years, were included. Nineteen nodules were differentiated thyroid carcinomas. On multivariate analysis, size was the only significant predictor of malignancy. On recursive partitioning analysis, size >35 mm with microcalcification and ill-defined margins yielded the best prediction model. Radiologist inter-observer agreement regarding malignancy was moderate (κ=0.50). CONCLUSIONS: Larger size, microcalcifications and ill-defined margins on ultrasound demonstrate the best predictive model for malignancy in the pediatric population. Experienced pediatric radiologists demonstrate moderate inter-observer agreement in prediction 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 | 0.010 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".