Cost-Effectiveness of Molecular Testing for Thyroid Nodules With Atypia of Undetermined Significance Cytology
Bibliographic record
Abstract
CONTEXT: Novel molecular diagnostics, such as the gene expression classifier (GEC) and gene mutation panel (GMP) testing, may improve the management for thyroid nodules with atypia of undetermined significance (AUS) cytology. The cost-effectiveness of an approach combining both tests in different practice settings in North America is unknown. OBJECTIVE: The aim of the study was to determine the cost-effectiveness of two diagnostic molecular tests, singly or in combination, for AUS thyroid nodules. DESIGN AND SETTING: We constructed a microsimulation model to investigate cost-effectiveness from US (Medicare) and Canadian healthcare system perspectives. PATIENTS: Low-risk patients with AUS thyroid nodules were simulated. INTERVENTIONS: We examined five management strategies: 1) routine GEC; 2) routine GEC + selective GMP; 3) routine GMP; 4) routine GMP + selective GEC; and 5) standard management. MAIN OUTCOME MEASURES: Lifetime costs and quality-adjusted life-years were measured. RESULTS: From the US perspective, the routine GEC + selective GMP strategy was the dominant strategy. From the Canadian perspective, routine GEC + selective GMP cost and additional CAN$24 030 per quality-adjusted life-year gained over standard management, and was dominant over the other strategies. Sensitivity analyses reported that the decisions from both perspectives were sensitive to variations in the probability of malignancy in the nodule and the costs of the GEC and GMP. The probability of cost-effectiveness for routine GEC + selective GMP was low. CONCLUSIONS: In the US setting, the most cost-effective strategy was routine GEC + selective GMP. In the Canadian setting, standard management was most likely to be cost effective. The cost of these molecular diagnostics will need to be reduced to increase their cost-effectiveness for practice settings outside the United States.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".