An international, multi-institution survey of the use of EUS in the diagnosis of pancreatic cystic lesions
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Currently, pancreatic cystic lesions (PCLs) are recognized with increasing frequency and have become a more common finding in clinical practice. EUS is challenging in the diagnosis of PCLs and evidence-based decisions are lacking in its application. This study aimed to develop strong recommendations for the use of EUS in the diagnosis of PCLs, based on the experience of experts in the field. METHODS: A survey regarding the practice of EUS in the evaluation of PCLs was drafted by the committee member of the International Society of EUS Task Force (ISEUS-TF). It was disseminated to experts of EUS who were also members of the ISEUS-TF. In some cases, percentage agreement with some statements was calculated; in others, the options with the greatest numbers of responses were summarized. RESULTS: Fifteen questions were extracted and disseminated among 60 experts for the survey. Fifty-three experts completed the survey within the specified time frame. The average volume of EUS cases at the experts' institutions is 988.5 cases per year. CONCLUSION: Despite the limitations of EUS alone in the morphologic diagnosis of PCLs, the results of the survey indicate that EUS-guided fine-needle aspiration is widely expected to become a more valuable method.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| 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".