Cytologic, histologic and molecular findings of papillary thyroid carcinoma variants, one institution’s experience
Bibliographic record
Abstract
Papillary thyroid carcinoma (PTC) has two major types, classic (PTCC) and follicular variant (FVPTC), which correlate with molecular findings and have varying clinical implications. We assessed the cytologic findings and subsequent surgical pathology findings with the molecular mutations in these two groups, including microcarcinomas. Fourty-four patients with PTC resections over a one-year period were retrospectively examined in conjunction with previous cytologic diagnoses. BRAF, NRAS and TERT promoter mutations for the resected specimens were analyzed. Correlation with previous cytology in regard to molecular mutations and tumor size (microcarcinoma) were made. Significantly more BRAF V600E mutations were seen with PTCC, whereas significantly more NRAS mutations were seen with FVPTC. TERT mutations were only seen with PTCC. Molecular studies for thyroid carcninomas are becoming increasingly more common and influence treatment and patient prognosis. BRAF and or TERT mutations are associated with a worse prognosis. NRAS mutations associated with FVPTC and may lead to milder cytologic changes compared to the BRAF- and TERT-driven PTCC.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".