Cost-Effectiveness of Thyroid Nodule Risk Stratification Guidelines
Bibliographic record
Abstract
The goal of this study is to evaluate the cost savings of consistently adhering to the 2017 American College of Radiology (ACR) Thyroid Imaging Reporting and Data System (TI-RADS) and the 2015 American Thyroid Association (ATA) criteria for the evaluation of thyroid nodules. In this retrospective study, 2 radiologists independently reviewed ultrasound (US) features of 291 cytology-proven thyroid nodules and scored them based on the ACR TI-RADS and ATA guidelines. The expected costs of strict adherence to recommendations based on the 2 risk stratification guidelines were calculated and compared with the actual cost to the health care system. Strict adherence to risk stratification guidelines can save the regional health care system up to $88,000 annually based on the 291 thyroid nodules examined. With retrospective application of ACR TI-RADS criteria, 51 nodules were recommended for follow-up US and 147 for fine-needle aspiration biopsy. With ATA criteria, 9 nodules were recommended for follow-up US, and 261 for fine-needle aspirations. Although fewer nodules were recommended for biopsy with TI-RADS criteria, the majority met criteria for follow-up US. Between the two guidelines, the ACR-TI-RADS offered slightly greater savings of ∼$3000 annually compared with ATA. Strict adherence to ACR TI-RADS and ATA guidelines can lead to substantial cost savings for the health care system by eliminating unnecessary thyroid biopsies. ACR TI-RADS is more cost-effective compared with ATA.
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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 | 0.009 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.002 | 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".