Surgical Outcomes of Thyroid Nodules Positive for Gene Expression Alterations Using ThyroSeq V3 Genomic Classifier
Bibliographic record
Abstract
ThyroSeq V3 (TsV3) tests for various genetic alterations, including gene expression alterations (GEAs), to improve diagnostic accuracy and clinical decision-making for indeterminate thyroid nodules. This study aimed to clarify the clinico-pathological features and outcomes of GEA-positive thyroid nodules, which have not yet been well-described in the literature. A retrospective chart review was performed whereby patients were included if they underwent thyroid surgery between January 2018 and May 2022 at two McGill University teaching hospitals and their surgery was preceded by pre-operative molecular TsV3 testing. In total, 75 of the 328 patients with thyroid nodules (22.9%) who underwent molecular testing and surgery were GEA-positive. On surgical pathology, GEA-positive nodules showed a significantly higher malignancy rate compared to their GEA-negative counterparts (90.7% vs. 77.7%, respectively, p = 0.011). Among those that were malignant, 48.5% had at least one aggressive pathological feature, including histological subtype, extra-thyroidal extension, or lymph node metastasis. BRAF V600E mutation had a significantly greater association with aggressive malignant GEA-positive nodules compared to non-aggressive ones (p < 0.001). This study demonstrates that GEA may be an effective diagnostic and prognostic tool for thyroid nodule management. However, further investigation is needed to characterize the clinico-pathological features of GEA in isolation and in association with other gene alterations.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".