Role of vandetanib in the management of medullary thyroid cancer
Bibliographic record
Abstract
Traditionally available treatments, like cytotoxic chemotherapy and external-beam radiation therapy, are limited and essentially ineffective for metastatic medullary thyroid carcinoma (MTC). In the last decade, small-molecule tyrosine kinase inhibitors (TKI) have been introduced in the field of thyroid cancer, after having been shown effective in a wide variety of other tumors. This review focuses on vandetanib (ZD6474, Zactima™; AstraZeneca) and its role in the treatment of MTC. Vandetanib is an oral TKI that targets VEGF receptors 2 and 3, RET, and at higher concentrations, the epidermal growth factor (EGF) receptor. This drug has been tested in two important phase II studies which demonstrated that both the 100 and 300 mg/day dosage of vandetanib have antitumor activity on advanced MTC. A phase III trial (ZETA trial) evaluating vandetanib in 331 patients with locally advanced or metastatic MTC showed a significant prolongation of PFS for patients receiving vandetanib compared with placebo. Toxicity surveillance in all studies reported high rates of adverse effects with diarrhea, rash, fatigue and nausea being the most commonly experienced by patients. Vandetanib is currently approved in the United States for unresectable locally advanced or metastatic MTC and has become a new standard of care in this rare and indolent pathology.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".