Diagnostic accuracy of the McGill thyroid nodule score in paediatric patients
Bibliographic record
Abstract
INTRODUCTION: Thyroid cancer is the most common paediatric endocrine cancer; accurate diagnosis and prompt management of paediatric thyroid nodules is critical. The McGill Thyroid Nodule Score (MTNS), based upon clinical, ultrasound (US) and cytology criteria, has recently been modified and studied in a pilot paediatric group with good results. We aim to describe the diagnostic accuracy of the paediatric modified MTNS (PMTNS) in a large paediatric cohort. METHODS: We utilized an established retrospective cohort between 1996 and 2015 of 99 patients ≤21 years old with 131 thyroid nodules. Two experienced paediatric radiologists, blinded to pathology and radiology reports, reviewed US features. We abstracted cytology, histology and laboratory results, assigning each nodule a PMTNS. PMTNS performance was compared to FNA and histology. RESULTS: Approximately 33% of nodules were malignant. The cohort was predominantly adolescent (mean age 15.4 ± 3.8 years). The average PMTNS for malignant and benign nodules, based on final histology, was 12.7 ± 4.3 and 1.7 ± 2.9, respectively. A PMTNS ≥8 resulted in a 93.2% sensitivity and 93.1% specificity for detecting malignancy, while a PMTNS ≥9 resulted in a 90.9% sensitivity and 96.6% specificity. However, Bethesda cytology category ≥4 independently had a 97.7% sensitivity and 94.0% specificity for detecting malignancy. The PMTNS had diminishing diagnostic accuracy in younger children compared with older children. CONCLUSION: Paediatric modified McGill Thyroid Nodule Score predicts malignancy, perhaps due to the score's emphasis on cytology results; however, the score is less accurate in younger patients. While cytology results remain reliable, further work is needed to develop a non-invasive scoring system to predict malignancy in children.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".