The McGill Thyroid Nodule Score's (MTNS+) role in the investigation of thyroid nodules with benign ultrasound guided fine needle aspiration biopsies: A retrospective review
Bibliographic record
Abstract
BACKGROUND: Ultrasound guided fine needle aspiration (USFNA) biopsies of thyroid nodules sometimes create a decision-making dilemma for surgeons as they may yield falsely benign results. The McGill Thyroid Nodule Score + (MTNS+) was developed to aid in clinical guidance regarding the management of patients with these USFNA results. The aim of this study was to assess the MTNS+ as a clinical tool in patients with benign preoperative thyroid nodule USFNAs and to analyze the relationship between nodule size and malignancy in these patients. METHODS: We conducted a retrospective chart review of 1312 patients who underwent thyroidectomies between 2010 and 2015 at the McGill University Teaching Hospitals. Patients with Bethesda II (benign) USFNA results, calculated MTNS+, and nodule size evaluated on ultrasound were included in the study. The false-negative rate was calculated, and MTNS+ and nodule size were each compared to final pathology results. Binary logistic regression was used for statistical analysis. RESULTS: Of the 1312 patients, 101 met the inclusion criteria and together had an average MTNS+ score of 6.83, which corresponds to a predicted malignancy rate between 25 and 33 %. Final pathology revealed malignancy in 16 (15.8 %) subjects. The average MTNS+ of patients with malignant nodules on surgical pathology was 8.25, while that of patients with benign nodules was 6.56. Patients with nodule size 1-1.9 cm (a) and 2-2.9 cm (b) each had an equal rate of malignancy of 2.97 % (n = 3), nodule size 3-3.9 cm (c) had a rate of 1.98 % (n = 2), and nodule size ≥4 cm (d) a rate of 7.92 % (n = 8). CONCLUSION: The rate of malignancy (15.8 %) is higher than expected when reviewing the risk of malignancy in nodules considered as Bethesda class 2. On the other hand, the rate is lower than the 25-33 % predicted by the MTNS+. We also found a higher malignancy rate for nodules above 4 cm in size, but size was a poor predictor of malignancy when used alone. Therefore, while the MTNS+ may be helpful at helping to identify USFNAs that are incorrectly classified as benign, the percentage risk of malignancy is lower than expected.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".