DIAGNOSIS OF ENDOCRINE DISEASE: Usefulness of genetic testing of fine-needle aspirations for diagnosis of thyroid cancer
Bibliographic record
Abstract
Objective: Genetic testing is increasingly used to diagnose or rule out thyroid cancer in indeterminate fine-needle aspirations. This review evaluates the usefulness of these methods with considerations of advantages and limitations. Design: Given the diagnostic problem associated with the increasing incidental detection of indeterminate thyroid nodules in the context of thyroid cancer overtreatment, we consider the conditions and respective necessary settings for the role of genetic testing to improve presurgical malignancy risk stratification. Methods: We review diagnostic pathway requirements and commercially available molecular tests with their respective advantages and disadvantages and discuss the prerequisites required for local application and implementation including quality assurance for local ultrasound and cytopathology practices. Results: Recent improvements in available molecular diagnostic tests have brought high sensitivity and specificity in initial validation studies, but whether these promising results translate to other clinical settings depends on the quality of the local thyroid nodule diagnostic pathway. Conclusions: Genetic testing can meaningfully improve presurgical malignancy risk assessment, but more work is needed to implement and use genetic testing effectively in local settings.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".