Initial Experiences of Selective RET Inhibitor Selpercatinib in Adults with Metastatic Differentiated Thyroid Carcinoma and Medullary Thyroid Carcinoma: Real-World Case Series in Korea
Bibliographic record
Abstract
-altered thyroid cancer. We present four cases of patients with advanced thyroid cancer who were treated with selpercatinib. The first patient was a 63-year-old male with advanced medullary thyroid cancer (MTC) treated with vandetanib. Six months ago, he had an intracranial hemorrhage and swallowing difficulty. He started selpercatinib with percutaneous endoscopic gastrostomy (PEG). For 11 months, a partial response (PR) was observed stably with PEG administration without any more cardiovascular events. The second patient was a 67-year-old female with advanced MTC treated with vandetatib. After selpercatinib treatment, a PR was observed for most metastatic sites, including choroidal metastasis. The third patient was a 32-year-old female with advanced papillary thyroid cancer (PTC) without history of systematic treatment. For six months, a PR was observed at her metastatic site with manageable adverse events. The last patient was a 59-year-old female with advanced PTC treated with lenvatinib. She suffered from a panic disorder and pleural pain due to metastasis during lenvatinib treatment. After selpercatinib treatment, her pain and panic symptoms were improved. Facing varying clinical obstacles of the real world, selpercatinib safely proved remarkable therapeutic efficacy regardless of previous treatment or metastatic site.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".