The Treatment of Thyroid Cancer With Radiofrequency Ablation
Bibliographic record
Abstract
In the past decade, there has seen been a shift from treating all thyroid cancer surgically, to favoring less aggressive approaches for low-risk thyroid cancer. Surgery was historically the treatment of choice for most thyroid cancer. Active surveillance has emerged as an alternative for low-risk thyroid cancer in select patients. This approach has been accepted worldwide, and sound evidence supports its oncological safety in carefully selected patients. However, not all patients want to undergo lifelong monitoring, and some patients may wish to treat their cancer in a minimally invasive manner. Thermal ablation has developed as a minimally invasive alternative to surgery and active surveillance for well selected patients with thyroid malignancy. Herein, we review the role of thermally ablative techniques, specifically radiofrequency ablation, for the treatment of small primary thyroid cancers, recurrent thyroid cancer, and lymph node metastases. In the past decade, there has seen been a shift from treating all thyroid cancer surgically, to favoring less aggressive approaches for low-risk thyroid cancer. Surgery was historically the treatment of choice for most thyroid cancer. Active surveillance has emerged as an alternative for low-risk thyroid cancer in select patients. This approach has been accepted worldwide, and sound evidence supports its oncological safety in carefully selected patients. However, not all patients want to undergo lifelong monitoring, and some patients may wish to treat their cancer in a minimally invasive manner. Thermal ablation has developed as a minimally invasive alternative to surgery and active surveillance for well selected patients with thyroid malignancy. Herein, we review the role of thermally ablative techniques, specifically radiofrequency ablation, for the treatment of small primary thyroid cancers, recurrent thyroid cancer, and lymph node metastases.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".