Effect of Having Concurrent Mutations on the Degree of Aggressiveness in Patients with Thyroid Cancer Positive for TERT Promoter Mutations
Bibliographic record
Abstract
This study aimed to examine whether concurrent mutations with a TERT promoter mutation are associated with a greater likelihood of more aggressive disease than a TERT promoter mutation alone. The medical records of 1477 patients who underwent thyroid surgery at two tertiary hospitals between 2017 and 2022 were reviewed. Twenty-four patients had TERT promoter mutations based on molecular profile testing. Clinicodemographic data, mutational profiles, and histopathological features were assessed. Descriptive analysis, Fisher’s exact test, and binary logistic regression were performed. Seven patients had single-gene TERT promoter mutations, and 17 had concurrent mutations, including BRAF V600E, HRAS, NRAS, PIK3CA, and EIF1AX. The overall prevalence of malignancy was 95.8%, of which 78.3% were aggressive thyroid cancers. There was a statistically significant association between concurrent mutations and disease aggressiveness. The odds of having aggressive disease were 10 times higher in patients with a TERT promoter mutation and a concurrent molecular alteration than in those with a TERT promoter mutation alone. This is an important finding for thyroid specialists to consider when counseling patients concerning risk stratification and management options.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".