Determining Patient Preferences for Indeterminate Thyroid Nodules: Observation, Surgery or Molecular Tests
Bibliographic record
Abstract
BACKGROUND: Gene-expression classifiers (GEC) and genetic mutation panels (GMP) have been shown to improve preoperative diagnostic evaluations of indeterminate thyroid nodules. Despite the improvement, uncertainty regarding the proper management exists. Patient preferences may better inform the management of these indeterminate thyroid nodules. METHODS: Hypothetical scenarios were administered to two groups of patients: those with previous FNA-confirmed indeterminate thyroid nodules (Group A, n = 50) and those presenting to a general otolaryngology clinic for other reasons (Group B, n = 50). We evaluated patient preferences for surgery, observation and the use of molecular tests while varying the risk of malignancy, cost and diagnostic properties of the tests. RESULTS: The mean threshold for choosing surgery over observation was a 38.6% risk of malignancy on FNA. When offered either GEC, GMP or both (with their inherent imperfect diagnostic properties) in addition to the indeterminate FNA, 85.0% of respondents picked at least one of the molecular tests over either observation or surgery if the test(s) were free of charge. However, only 51.7% of respondents chose at least one of the tests when asked to pay the current cost of the test(s) (p < 0.001). On multivariable analysis, sex, the presence of an indeterminate FNA diagnosis and income level significantly predicted the desire to proceed with a molecular test above standard management. CONCLUSION: Patient preferences for thyroid nodule management are dependent on the risk of malignancy, prognosis of cancer and costs. Patients prefer molecular tests over standard management with indeterminate thyroid nodules, but the costs of the test(s) reduce the desire.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".