Local molecular analysis of indeterminate thyroid nodules
Bibliographic record
Abstract
BACKGROUND: Thyroid nodules are common but only a minority are malignant. Molecular testing can assist in helping determine whether indeterminate nodules are suspicious for malignancy or benign. The objective of the study was to determine if the analysis of mutations (BRAF, NRAS, KRAS and HRAS) using readily available molecular techniques can help better classify indeterminate thyroid nodules. METHODS: A retrospective cohort of consecutive patients undergoing diagnostic thyroid surgery were analyzed for the presence or absence of specific mutations known to be associated with thyroid malignancy in FNA samples. Markers chosen were BRAF, NRAS, KRAS and HRAS. All were locally available and currently in use at our centre for other clinical indications. Results from the molecular analysis were then compared to the histopathology from thyroidectomy specimens to determine the sensitivity and specificity of these molecular techniques to classify indeterminate thyroid nodules. RESULTS: Sixty consecutive patients with indeterminate FNAs were recruited. Twenty-three patients had malignant tumors while 37 specimens were benign. Multiple different mutations were identified in the FNA samples. Overall 18 cases had a positive mutation (10 malignant and 8 benign). The sensitivity of BRAF, HRAS, KRAS, and NRAS was 8.7, 8.7, 8.7, and 17.4 respectively while the specificity was100, 83.7, 100 and 94.6. CONCLUSION: While molecular analysis remains promising, it requires further refinement. Several markers showed promise as good "rule-in" tests.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".