Consensus Statement: Recommendations on Actionable Biomarker Testing for Thyroid Cancer Management
Bibliographic record
Abstract
Thyroid cancer management is rapidly changing. The identification of actionable biomarkers through both germline and somatic testing are now an integral part of directing patient management. However, deficiencies and disparities within existing thyroid cancer biomarker test approaches are resulting in inconsistent application for patient care. An expert panel was convened to create consensus biomarker testing algorithms and recommendations on actionable biomarker testing for patients diagnosed with medullary thyroid cancer, non-anaplastic follicular cell-derived thyroid cancer, or anaplastic follicular cell-derived thyroid cancer who may benefit from targeted therapies. A review of international guidelines was performed to determine the current state, and a literature review was carried out to further evaluate the evidence supporting the use of actionable biomarkers in patients diagnosed with thyroid cancer. Thyroid biomarker-related gaps impacting patient care were also discussed, with an emphasis on the importance of a multidisciplinary team approach for optimal patient care. The recommendations are presented with the aim to help physicians navigate the current thyroid cancer biomarker testing landscape with its many challenges, balancing aspirational care with what is practical and feasible in terms of economic realities and jurisdictional constraints. By remaining therapy-agnostic, these algorithms and recommendations are broadly applicable.
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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 | 0.063 | 0.149 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.009 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.009 | 0.006 |
| Research integrity | 0.023 | 0.019 |
| Insufficient payload (model declined to judge) | 0.015 | 0.014 |
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".