Does the likelihood of malignancy in thyroid nodules with RAS mutations increase in direct proportion with the allele frequency percentage?
Bibliographic record
Abstract
BACKGROUND: Genomic testing has enhanced pre-surgical decision making for cytologically indeterminate thyroid nodules, but there remains uncertainty regarding RAS mutations. The addition of extra genetic alterations to previous driver mutation panels has been shown to improve predictive value. This study aims to evaluate the relationship between the mutant allele frequency (AF) and likelihood of malignancy in thyroid nodules with RAS mutations. METHODS: A retrospective cohort review was performed evaluating patients with indeterminate cytology (Bethesda categories III, IV and V) and ThyroSeq® v3 testing demonstrating a RAS mutation, who underwent surgery. Univariate and multivariate regression analyses were used to evaluate relationships between AF, other genetic alterations, and malignancy. RESULTS: Thirty-nine patients met criteria, 77% of the thyroid nodules (30/39) were found to be malignant. None demonstrated aggressive pathology. On univariate regression, there was no relationship between AF and likelihood of malignancy. There was, however, a significant correlation between AF and the rate of an additional genetic alteration. Multivariate analysis found a trend between RAS, a second genetic alteration and malignancy, but it did not reach statistical significance. CONCLUSIONS: There was no direct relationship between the level of allelic frequency in thyroid nodules expressing RAS mutations and the likelihood of malignancy. There was a statistically significant relationship between increasing AF and the presence of a second genetic abnormality, suggesting a possible progression from initial driver mutation and then a second genetic alteration prior to malignant transformation.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".