Cost-effectiveness analysis of molecular testing for cytologically indeterminate thyroid nodules
Bibliographic record
Abstract
BACKGROUND: Thyroid nodules affect up to 65% of the population. Although fine needle aspirate (FNA) cytology is the gold standard for diagnosis, 15-30% of results are indeterminate. Molecular testing may aid in the diagnosis of nodules and potentially reduce unnecessary surgery. However, these tests are associated with significant costs. The objective of this study was to evaluate the cost-effectiveness of Afirma, a commercially available molecular test, in cytologically indeterminate thyroid nodules. METHODS: The base case was a solitary thyroid nodule with no additional high-risk features and an indeterminate FNA. Decision tree analysis was performed from the single payer perspective with a 1-year time horizon. Costing data were collected through micro-costing methodology. A probabilistic sensitivity analysis was performed. The primary outcome was the incremental cost effectiveness ratio (ICER) of cost per thyroid surgery avoided. RESULTS: Over 1 year, mean cost estimates were $8176.28 with 0.58 effectiveness for the molecular testing strategy and $6016.83 with 0.07 effectiveness for current standard management. The ICER was $4234.22 per surgery avoided. At a willingness-to-pay (WTP) threshold of $5000 per surgery avoided, molecular testing is cost-effective with 63% certainty. CONCLUSION: This cost-effectiveness analysis suggests utilizing Afirma for indeterminate solitary thyroid nodules is a cost-effective strategy for avoiding unnecessary thyroid surgery. With a $5000 WTP threshold, molecular testing has a 63% chance of being the more cost-effective strategy. The cost effectiveness varies based on the cost of the molecular test and the value of Afirma for patients with indeterminate thyroid nodules depends on the WTP threshold to avoid unnecessary thyroid surgery.
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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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".