Anaplastic thyroid cancer and primary thyroid lymphoma: A review of these rare thyroid malignancies
Bibliographic record
Abstract
BACKGROUND: To review the current literature on the treatment of anaplastic thyroid cancer (ATC) and thyroid lymphoma (TL). RESULTS: Both anaplastic carcinoma (ATC) and TL represent rare forms of thyroid cancer. ATC behaves in a highly aggressive manner, resulting in significant morbidity and mortality. Multimodality therapy consisting of both radiotherapy (RT) and chemotherapy is essential in obtaining local/regional control. Although ATC has been relatively chemo resistant, newer agents such like taxotere show promise. The role of surgery in the treatment of ATC continues to evolve, presently it should be reserved for patients who have shown an initial response to multimodality therapy and in patients in whom a complete macroscopic resection can be achieved with minimal morbidity. The successful treatment of TL currently lies in accurately diagnosing the histological subtype. Both large B-cell and mixed lymphomas are best treated with multimodality therapy consisting of CHOP combined with hyper-fractioned RT. MALT lymphomas with there more indolent course may be amenable to single modality RT or total thyroidectomy if diagnosed at an early stage IE. DISCUSSION: Although both ATC and TL are rare, it is important for surgeons to be aware of the need for multimodality therapy when treating these patients and to understand the limited role surgery plays in diagnosis and treatment.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".