Braf Mutation Correlates with Recurrent Papillary Thyroid Carcinoma in Chinese Patients
Bibliographic record
Abstract
PURPOSE: We investigated correlations of somatic BRAF V600E mutation and RET/PTC1 rearrangement with recurrent disease in Chinese patients with papillary thyroid carcinoma (ptc). METHODS: This prospective study included 214 patients with ptc histologically confirmed between November 2009 and May 2011 at a single institute. RESULTS: We found somatic BRAF V600E mutation in 68.7% and RET/PTC1 rearrangement in 25.7% of the patients. Although BRAF mutation was not significantly associated with clinicopathologic features such as patient sex or age, multicentric disease, thyroid capsule invasion, tumour stage, or nodal metastasis, it was significantly associated with recurrent disease. Multivariate analysis revealed that BRAF mutation and tumour size were independent risk factors associated with recurrent disease, with odds ratios of 9.072 and 2.387 respectively. The area under the receiver operating characteristic curve increased 8.3% when BRAF mutation was added to the traditional prognostic factors, but that effect was statistically nonsignificant (0.663 vs. 0.746, p = 0.124). RET/PTC1 rearrangement and nodal metastasis were significantly associated in all patients (p = 0.042), marginally associated in ptc patients (p = 0.051), but not associated in microptc patients (p = 0.700). RET/PTC1 rearrangement was not significantly associated with recurrent disease. CONCLUSIONS: BRAF positivity is an independent predictor of recurrent disease in ptc.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".