Serum Thyroglobulin Improves the Sensitivity of the McGill Thyroid Nodule Score for Well-Differentiated Thyroid Cancer
Bibliographic record
Abstract
BACKGROUND: The McGill Thyroid Nodule Score (MTNS) is a scoring system devised to help physicians to assess the preoperative risk that a thyroid nodule is malignant. It uses 22 different known risk factors for thyroid cancer (radiation exposure, microcalcifications on ultrasound, positive HBME-1 stain on biopsy, etc.) and attributes a percentage risk that the nodule is malignant. Recently, preoperative thyroglobulin (Tg) levels have been shown to correlate with the risk of malignancy. The aim of this study was to incorporate Tg levels into the already established MTNS. METHODS: This is a retrospective analysis of 184 thyroidectomy patients at the McGill University Thyroid Cancer Center. Patients with preoperative Tg levels were included in the study, and patients with incidental papillary microcarcinoma without extrathyroidal extent on final pathology were excluded. MTNS scores were calculated for all patients. Preoperative Tg levels of 75 ng/mL added one point to the MTNS, and levels of 187.5 ng/mL added two points. The new system is named MTNS+. RESULTS: Malignancy rates were calculated for each MTNS+ score. Patients with a score of 0-1 were <5% at risk of malignancy. The malignancy rate for scores of 2-3 was 14.29%, followed by 28.95% for scores of 4-6, 32.65% for scores of 7-8, 64.86% for scores of 9-11, 71.43% for scores of 12-14, 78.57% for scores of 15-18, and 92.31% for scores of 19-22. All patients (five of five) with an MTNS+ score of 23 or more had a malignant final pathology result. Patients with scores greater than eight had a relative risk of 2.5 [CI 1.79-3.49] of malignancy compared to patients with lower scores. MTNS+ showed good specificity at higher scores, with 89%, 96%, and 100% at scores above 11, 14, and 20 respectively. Compared to MTNS, adding Tg levels did not improve positive predictive values (PPV) or specificity, but improved sensitivity by 7.89% for scores greater than eight, and by up to 10.48% for scores greater than seven. CONCLUSION: This study shows that adding Tg to the MTNS increases the sensitivity of this scoring system. Moreover, it suggests that a combined scoring system such as the MTNS+ can accurately stratify the risk of well-differentiated malignancy in patients with thyroid nodules.
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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 | 0.006 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".