Investigating the Applicability of the McGill Thyroid Nodule Score (MTNS) in Patients Undergoing Surgery for Thyroid Nodules: A Comparison Between Patients with and Without Papillary Thyroid Cancer
Bibliographic record
Abstract
Objective: Various algorithms are currently used to evaluate patients with thyroid nodules. Thyroid fine needle aspiration biopsy (FNAB) is the most valuable method for assessment, but it yields 5-10% false negative results. Therefore, the McGill Thyroid Nodule Score (MTNS), which consists of 22 parameters, was developed for use in the preoperative period. In this study, we investigated the applicability of MTNS in patients with indeterminate FNAB results by comparing patients diagnosed with papillary thyroid cancer to those with benign outcomes according to the specimen result. Materials and Methods: Between January 2016 and August 2017, 382 patients who underwent thyroidectomy at our clinic were evaluated. A total of 140 patients categorized as Bethesda III- IV-V were included in the study. The MTNS was calculated and compared between the malignant and benign groups. Subsequently, patients were divided into four groups based on nodule diameter to evaluate their MTNS. Results: The median MTNS was 6 (range 1-16) in the benign group and 12 (range 3-23) in the malignant group. To determine the cut-off for MTNS, Receiver Operating Characteristic (ROC) analysis was conducted using the pathology result as the primary endpoint. The cut-off value was determined to be 8.5. Statistical analysis revealed that the sensitivity and specificity of MTNS were 83.0% and 85.5%, respectively. Conclusion: Ultrasonography (USG) and FNAB are currently preferred methods for approaching patients with thyroid nodules. In cases where FNAB results are inconclusive, MTNS can be safely applied clinically to identify high-risk patients.
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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 | 0.001 | 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".