A novel guided approach to radiofrequency ablation of thyroid nodules: the Toronto Sunnybrook experience
Bibliographic record
Abstract
Introduction: Thyroid nodules are extremely common being detected by ultrasonography in up to 67% of the population, with current surgical tenet maintaining that lobectomy is required for large symptomatic benign nodules or autonomously functionally nodules resulting in a risk of hypothyroidism or recurrent laryngeal nerve injury even in high volume centres. The introduction of radiofrequency ablation (RFA) has allowed thermal ablation of both benign and autonomously functioning thyroid nodules with minimal morbidity. The moving shot technique is the most well-established technique in performing RFA of thyroid nodules, and has proven to be safe, efficacious, accurate and successful amongst experienced clinicians. The purpose of this article to propose the use of a novel guide when performing RFA of thyroid nodules in clinical practice utilizing the moving shot technique. Methods: The technique proposed of RFA involves the use of a 10MHz linear ultrasound probe attached to an 18G guide which provides robust in line visualisation of a 7cm or 10cm radiofrequency probe tip (STARmed, Seoul, Korea) utilizing the trans isthmic moving shot technique. A geometric analysis of the guide has been illustrated diagrammatically. Results: , facilitating efficient and complete ablation of conceptual subunits during RFA of thyroid nodules. Discussion: Radiofrequency ablation of thyroid nodules can be performed safely and effectively using the novel radiofrequency probe guide proposed which we believe potentially improves both accuracy and overall efficiency, along with operator confidence in maintaining visualisation of the probe tip, and hence we believe provides a valuable addition to the armamentarium of clinicians wishing to embark on performing RFA of thyroid nodules.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".