Genomically Driven Precision Medicine to Improve Outcomes in Anaplastic Thyroid Cancer
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Thyroid cancer is an endocrine malignancy with an incidence rate that has been increasing steadily over the past 30 years. While well-differentiated subtypes have a favorable prognosis when treated with surgical resection and radioiodine, undifferentiated subtypes, such as anaplastic thyroid cancer (ATC), are far more aggressive and have a poor prognosis. Conventional therapies (surgical resection, radiation, chemotherapy, and radioiodine) have been utilized for treatment of ATC, yet these treatments have not significantly improved the overall mortality rate. As cancer is a genetic disease, genetic alterations such as mutations, fusions, activation of oncogenes, and silencing of tumor suppressors contribute to its aggressiveness. With the use of next-generation sequencing and the Cancer Genome Atlas, mutation-directed therapy is recognized as the upcoming standard of care. In this review, we highlight the known genetic landscape of ATC and the need for a comprehensive genetic characterization of this disease in order to identify additional therapeutic targets to improve patient outcomes.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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 it