The Mcgill thyroid nodule score – does it help with indeterminate thyroid nodules?
Bibliographic record
Abstract
BACKGROUND: Ultrasound guided fine-needle aspiration (USFNA) biopsy of thyroid nodules often gives a result of indeterminate pathology, placing thyroid specialists in difficult management situations. The aim of this study is to evaluate the incidence of malignancy in patients undergoing surgery and to correlate these results with the McGill Thyroid Nodule Score (MTNS). METHODS: We performed a retrospective study comparing USFNA results, MTNS and histopathology of patients undergoing thyroid surgery between 2010 and 2012. Pre-operative USFNA results were divided into three subgroups: benign, indeterminate and suspicious for/malignant. The indeterminate USFNA subgroup comprised of Bethesda type III (atypia of undetermined significance) and Bethesda type IV (follicular neoplasms, including Hurthle cell neoplasms) lesions. Post-operative histopathology was divided into benign or malignant groups. RESULTS: Of the 437 patient charts reviewed, 57.0% had an indeterminate USFNA biopsy. Within the indeterminate group, the malignancy rate was 39.8%. For indeterminate USFNA, the median MTNS was 7 (32% risk of malignancy) for benign nodules and 9 (63% risk of malignancy) for malignant nodules on post-operative histopathology (p < 0.05). CONCLUSION: The rate of malignancy in operated patients with an indeterminate USFNA result was 39.8%. The MTNS can be of value to thyroid specialists in pre-operative decision-making when dealing with an indeterminate result of a thyroid nodule on USFNA.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".