An international, multi-institution survey on performing EUS-FNA and fine needle biopsy
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Endoscopic ultrasound-guided fine needle aspiration (EUS-FNA) and fine needle biopsy (FNB) are effective techniques that are widely used for tissue acquisition. However, it remains unclear how to obtain high-quality specimens. Therefore, we conducted a survey of EUS-FNA and FNB techniques to determine practice patterns worldwide and to develop strong recommendations based on the experience of experts in the field. METHODS: This was a worldwide multi-institutional survey among members of the International Society of EUS Task Force (ISEUS-TF). The survey was administered by E-mail through the SurveyMonkey website. In some cases, percentage agreement with some statements was calculated; in others, the options with the greatest numbers of responses were summarized. Another questionnaire about the level of recommendation was designed to assess the respondents' answers. RESULTS: ISEUS-TF members developed a questionnaire containing 17 questions that was sent to 53 experts. Thirty-five experts completed the survey within the specified period. Among them, 40% and 54.3% performed 50-200 and more than 200 EUS sampling procedures annually, respectively. Some practice patterns regarding FNA/FNB were recommended. CONCLUSION: This is the first worldwide survey of EUS-FNA and FNB practice patterns. The results showed wide variations in practice patterns. Randomized studies are urgently needed to establish the best approach for optimizing the FNA/FNB procedures.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".