Initial experience with a novel EUS-guided core biopsy needle (SharkCore): results of a large North American multicenter study
Bibliographic record
Abstract
BACKGROUND AND AIMS: The ability to safely and effectively obtain sufficient tissue for pathologic evaluation by using endoscopic ultrasound (EUS) guidance remains a challenge. Novel designs in EUS needles may provide for improved ability to obtain such core biopsies. The aim of this study was to evaluate the diagnostic yield of core biopsy specimens obtained using a novel EUS needle specifically designed to obtain core biopsies. PATIENTS AND METHODS: Multicenter retrospective review of all EUS-guided fine-needle biopsies obtained using a novel biopsy needle (SharkCore FNB needle, Medtronic, Dublin, Ireland). Data regarding patient demographics, lesion type/location, technical parameters, and diagnostic yield was obtained. RESULTS: A total of 250 lesions were biopsied in 226 patients (Median age 66 years; 113 (50 %) male). Median size of all lesions (mm): 26 (2 - 150). Overall, a cytologic diagnosis was rendered in 81 % specimens with a median number of 3 passes. When rapid onsite cytologic evaluation (ROSE) was used, cytologic diagnostic yield was 126/149 (85 %) with a median number of 3 passes; without ROSE, cytologic diagnostic yield was 31/45 (69 %, P = 0.03) with a median number of 3 passes. Overall, a pathologic diagnosis was rendered in 130/147 (88 %) specimens with a median number of 2 passes. Pathologic diagnostic yield for specific lesion types: pancreas 70/81 (86 %), subepithelial lesion 13/15 (87 %), lymph node 26/28 (93 %). Ten patients (10/226, 4 %) experienced adverse events: 4 acute pancreatitis, 5 pain, 1 fever/cholangitis. CONCLUSIONS: Initial experience with a novel EUS core biopsy needle demonstrates excellent pathologic diagnostic yield with a minimum number of passes.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".