Publicly Funded Molecular Testing of Indeterminate Thyroid Nodules: Canada's Experience
Bibliographic record
Abstract
CONTEXT: Indeterminate thyroid nodules (ITNs) lead to diagnostic surgeries in many countries. The use of molecular testing (MT) is endorsed by several guidelines, but costs are limitative, especially in public health care systems like in Canada. OBJECTIVES: The primary objective of this work was to evaluate the clinical value of ThyroSeq v3 (TSv3) using benign call rate (BCR) in a real-world practice. The secondary objective was to assess the cost-effectiveness of MT. METHODS: This multicentric prospective study was conducted in 5 academic centers in Quebec, Canada. A total of 500 consecutive patients with Bethesda III (on 2 consecutive cytopathologies) or IV and TIRADS 3 or 4 nodules measuring 1 to 4 cm were included. MT was performed between November 2021 and November 2022. Patients with a positive TSv3 were referred for surgery. Patients with a negative TSv3 were planned for follow-up by ultrasonography for a minimum of 2 years. The BCR, corresponding to the proportion of ITNs with negative TSv3 results, was assessed. RESULTS: A total of 500 patients underwent TSv3 testing, with a BCR of 72.6% (95% CI, 68.5%-76.5%; P < .001). Ultimately, 99.7% of patients with a negative result avoided surgery. The positive predictive value of TSv3 was 68.2% (95% CI, 58.5%-76.9%). The cost-benefit analysis identified that the implementation of MT would yield a cost savings of $6.1 million over the next 10 years. CONCLUSION: The use of MT (TSv3) in a well-selected population with ITNs led to a BCR of 72.6%. It is cost-effective and prevents unnecessary surgeries in a public health care setting.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".