Bibliographic record
Abstract
Abstract. Thyroid cancer, which has a high prevalence of BRAF V600E mutations, is the ninth most frequent cancer worldwide, especially in the aggressive anaplastic thyroid cancer (ATC). The mutation causes continual activation of the MAPK pathway, resulting in unrestricted cell multiplication that leads to the development of tumors. The therapeutic potential of a selective BRAF V600E inhibitor, dabrafenib, has been approved in the treatment of thyroid cancer, especially ATC. The efficacy of dabrafenib was investigated by analyzing various in vivo and in vitro studies and clinical trials. The therapeutic role of dabrafenib on thyroid cancer has been approved in studies involving tumor growth, cell viability, apoptosis, and effects on cancer stem cells (CSCs). Notably, the combination of dabrafenib and MEK inhibitors such as trametinib. significantly inhibited tumor growth and induced apoptosis in thyroid cancer cells. Studies have shown that this combination therapy can reduce tumor volume and target CSCs, which are key to tumor recurrence and drug resistance. Clinical trials have reported improvements in patient status. Dabrafenib, especially in combination with other drugs, offers a better treatment strategy for aggressive thyroid cancer. It enhances the therapeutic effect by effectively targeting the MAPK pathway and CSCs. Future studies may focus on optimizing these combination therapies and exploring their effects and safety to further improve patient outcomes in thyroid cancer treatment.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".