Differentiated thyroid cancers: a comprehensive review of novel targeted therapies
Bibliographic record
Abstract
Differentiated thyroid carcinoma (DTC) accounts for more than 90% of new thyroid cancer diagnoses, and includes papillary, follicular and Hürthle cell carcinoma. The prognosis for the vast majority of individuals diagnosed with DTC is excellent, with current treatment that includes surgery, radioactive iodine ablation and postoperative thyroid-stimulating hormone suppression. Unfortunately, the small proportion of individuals who develop radioactive iodine-resistant recurrent disease have few treatment options, and the vast majority will eventually die from their disease. Recently, several novel targets for anticancer agents have been identified and offer new hope for thyroid cancer patients diagnosed with progressive disease. In addition to targeting genes commonly altered in thyroid cancer, which include mutations in BRAF, RAS and RET, proangiogenic growth factor receptors and the sodium-iodide symporter have also been targeted. Several clinical trials evaluating tyrosine kinase and angiogenesis inhibitors for treatment of individuals diagnosed with metastatic or treatment-refractory DTC are currently underway. The objective of this review is to evaluate recent clinical trials that have studied novel targeted drugs for treatment of DTC.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".