Cost-Effectiveness of the ACR TIRADS Compared to the ATA 2015 Risk Stratification Systems in the Evaluation of Incidental Thyroid Nodules
Bibliographic record
Abstract
RATIONALE AND OBJECTIVES: Thyroid nodules are a common incidental imaging finding and prone to overdiagnosis. Several risk stratification systems have been developed to reduce unnecessary work-up, with two of the most utilized including the American Thyroid Association 2015 (ATA2015) and the newer American College of Radiology Thyroid Imaging, Reporting and Data System (TIRADS) guidelines. The purpose of this study is to evaluate the cost-effectiveness of the ATA2015 versus the TIRADS guidelines in the management of incidental thyroid nodules. METHODS: A cost-utility analysis was conducted using decision tree modeling, evaluating adult patients with incidental thyroid nodules < 4 cm. Model inputs were populated using published literature, observational data, and expert opinion. Single-payer perspective, Canadian dollar currency, five-year time horizon, willingness to pay (WTP) threshold of $50,000, and discount rate of 1.5% per annum were utilized. Scenario, deterministic and probabilistic sensitivity analyses were performed. The primary outcome was the incremental cost-effectiveness ratio (ICER) expressed as incremental cost per quality-adjusted life year (QALY) gained. RESULTS: For the base case scenario, TIRADS dominated the ATA2015 strategy by a slim margin, producing 0.005 more QALYs at $25 less cost. Results were sensitive to the malignancy rate of biopsy and the utilities of a patient with a benign nodule/subclinical malignancy or under surveillance. Probabilistic sensitivity analysis showed that TIRADS was the more cost-effective option 79.7% of the time. CONCLUSION: The TIRADS guidelines may be the more cost-effective strategy by a small margin compared to ATA2015 in most scenarios when used to risk stratify incidental thyroid nodules.
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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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".