From MAiD Referral to Targeted Therapy Success: A Case of BRAF-Mutated Anaplastic Thyroid Cancer
Bibliographic record
Abstract
Background and Clinical Significance: Anaplastic thyroid cancer (ATC) is a rare and aggressive malignancy with a poor prognosis, where median survival typically ranges from 4 to 10 months. Advances in genetic profiling, particularly the identification of BRAF mutations, offer new opportunities for targeted therapy. Case Presentation: This case report details the journey of a woman in her late 50s diagnosed with symptomatic ATC. Initial immunohistochemistry (IHC) testing for BRAF mutations returned negative results, leaving the patient with limited treatment options and prompting her to pursue medical assistance in dying (MAiD). However, next-generation sequencing (NGS) confirmed a V600EBRAF mutation, and a basis for targeted therapy. The patient began treatment with dabrafenib-trametinib, followed by pembrolizumab as second-line therapy, ultimately extending her life by nearly seven months. Conclusions: This case underscores the importance of rapid and comprehensive diagnostic approaches, particularly the higher sensitivity of NGS over IHC for detecting BRAF mutations. The complexities of accessing newer therapies in Canada’s single-payer healthcare system are also emphasized. The utilization of newer rapid diagnostic technologies can have a direct impact on directing treatment for ATC and other aggressive malignancies.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".