McGill Thyroid Nodule Score in Differentiating Benign and Malignant Pediatric Thyroid Nodules: A Pilot Study
Bibliographic record
Abstract
Objective The McGill Thyroid Nodule Score (MTNS) is a preoperative tool used to predict the risk for well-differentiated thyroid cancer given a specific nodule in adults. We evaluated the clinical utility of a modified pediatric MTNS with children and adolescents. Study Design Case series with chart review. Setting Tertiary care children's hospital. Subjects and Methods This is a retrospective chart review of 46 patients ≤18 years of age presenting with a solitary or dominant thyroid nodule treated with surgical resection between September 2008 and December 2015. The cumulative MTNS for each nodule was calculated and compared with the final pathology. Results Of 46 patients, 10 (21.7%) were diagnosed with well-differentiated thyroid cancer (80% papillary thyroid carcinoma, 10% follicular variant of papillary thyroid carcinoma, 10% follicular thyroid carcinoma). Malignant nodules were associated with a greater mean MTNS (benign, 5.72 ± 3.03; malignant, 16 ± 3.13; P < .05). The sensitivity, specificity, and positive predictive value of malignancy were 100%, 94.4%, and 83.3% for scores ≥10 and 80%, 100%, and 100% for scores ≥11, respectively. In nodules with indeterminate cytology (Bethesda III and IV), the pediatric MTNS showed good differentiation between benign and malignant disease, with mean scores of 7.95 and 12.5, respectively ( P = .006). Conclusion This pilot study suggests that a comprehensive scoring system may help assess the risk of malignancy in pediatric thyroid nodules and differentiate nodules with indeterminate cytology into higher- and lower-risk categories. Given these findings, larger, multi-institutional studies are warranted.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".