Molecular testing for thyroid nodules: the experience at a McGill University Teaching Hospital in Canada
Bibliographic record
Abstract
In the past few decades, molecular characterization of thyroid cancer has made significant progress and is able to identify thyroid cancer related molecular markers which can then be applied clinically for improved decision making. The aim of this review is to provide a general overview about the molecular markers (mutations and alterations) of thyroid cancers, present several molecular tests, and discuss the clinical applications of identifying these markers supported by the clinical experience of several high-volume thyroid cancer specialists at the McGill university hospitals in Montreal, Canada. Our group experience showed that molecular testing can reclassify more than half of the patients with indeterminate thyroid nodules (Bethesda III and IV) into benign and spare these patients from unnecessary diagnostic surgery. Furthermore, it can help optimize the initial management in thyroid cancers with no evidence of high-risk of recurrence disease preoperatively. While routine molecular testing is not firmly established for thyroid FNA specimens that are suspicious or positive for malignancy (Bethesda V and VI), knowledge of a thyroid nodule’s molecular risk group profile in such cases, together with its clinical and radiologic features, can help select the optimal surgical options (lobectomy versus upfront total thyroidectomy and central neck dissection), as demonstrated by our studies
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".