Endoscopic ultrasound (EUS)-guided fine needle biopsy alone vs. EUS-guided fine needle aspiration with rapid onsite evaluation in pancreatic lesions: a multicenter randomized trial
Bibliographic record
Abstract
Abstract Background Endoscopic ultrasound-guided fine-needle aspiration (EUS-FNA) is the standard in the diagnosis of solid pancreatic lesions, in particular when combined with rapid onsite evaluation of cytopathology (ROSE). More recently, a fork-tip needle for core biopsy (FNB) has been shown to be associated with excellent diagnostic yield. EUS-FNB alone has however not been compared with EUS-FNA + ROSE in a large clinical trial. Our aim was to compare EUS-FNB alone to EUS-FNA + ROSE in solid pancreatic lesions. Methods A multicenter, non-inferiority, randomized controlled trial involving seven centers was performed. Solid pancreatic lesions referred for EUS were considered for inclusion. The primary end point was diagnostic accuracy. Secondary end points included sensitivity/specificity, mean number of needle passes, and cost. Results 235 patients were randomized: 115 EUS-FNB alone and 120 EUS-FNA + ROSE. Overall, 217 patients had malignant histology. The diagnostic accuracy for malignancy of EUS-FNB alone was non-inferior to EUS-FNA + ROSE at 92.2 % (95 %CI 86.6 %–96.9 %) and 93.3 % (95 %CI 88.8 %–97.9 %), respectively (P = 0.72). Diagnostic sensitivity for malignancy was 92.5 % (95 %CI 85.7 %–96.7 %) for EUS-FNB alone vs. 96.5 % (93.0 %–98.6 %) for EUS-FNA + ROSE (P = 0.46), while specificity was 100 % in both. Adequate histological yield was obtained in 87.5 % of the EUS-FNB samples. The mean (SD) number of needle passes and procedure time favored EUS-FNB alone (2.3 [0.6] passes vs. 3.0 [1.1] passes [P < 0.001]; and 19.3 [8.0] vs. 22.7 [10.8] minutes [P = 0.008]). EUS-FNB alone cost on average 45 US dollars more than EUS-FNA + ROSE. Conclusion EUS-FNB alone is non-inferior to EUS-FNA + ROSE and is associated with fewer needle passes, shorter procedure time, and excellent histological yield at comparable cost.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".