Endosonography-guided Radiofrequency Ablation in Pancreatic Diseases
Bibliographic record
Abstract
Over the past 20 years, endoscopic ultrasound-guided radiofrequency ablation (EUS-RFA) has generated interest as a novel minimally invasive tool in the multimodal treatment of pancreatic malignant and premalignant lesions. However, although optimization of probes and settings has made EUS-RFA relatively safe, questions on the ideal positioning of this treatment in a multimodal strategy remain unanswered. This review will summarize the technical aspects of EUS-RFA and available clinical experiences for each pancreatic indication (pancreatic cancer, neuroendocrine neoplasms, cystic lesions, and celiac ganglia neurolysis). Established indications will be discussed along those requiring additional clinical data or even proof-of-concept studies. A dedicated session will further discuss evidence expected to emerge from ongoing registered trials, together with issues that must be addressed in future research, including the possible combination with immunotherapy, and the personalization of this treatment on the basis of genetic profiling. Despite the great clinical enthusiasm and scientific fervor, while evidence-based answers are produced, EUS-RFA must be centralized in high-volume centers of recognized expertise, where multidisciplinary discussions of indications and actively recruiting research protocols are available.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".