A253 DETERMINATION OF OPTIMAL NUMBER OF ACTUATIONS FOR AN EUS GUIDED FNA PASS
Bibliographic record
Abstract
Endoscopic ultrasound (EUS) guided Fine Needle Aspiration (FNA) is an effective modality for tissue acquisition in the diagnosis of GI and non-GI pathology. The optimal method of FNA is not known. The main focus is on maximisation of each FNA pass. There is little emphasis on the number of times or actuations the needle is moved within the target lesion during each FNA pass. The aim of this study was to determine the optimal number of actuations for obtaining high diagnostic yield in each FNA pass. This is a retrospective study carried out at Vancouver General Hospital. EUS guided FNA of solid lesions in the GI tract were included. FNA was performed with a 22G FNA needle. Three different passes were performed on each mass lesion. A standardised technique was adopted for all passes which involved no stylet, fanning, and wet suction at 20cc. There were 10 actuations performed for the 1st pass, 20 actuations for the 2nd pass, and 30 actuations for the 3rd pass. The cellularity and diagnosis for each pass were determined by GI pathologists blinded to patient inclusion in the study. 32 patients were included in this study with 10 males and 22 females. The average age was 68 years with a median age of 67.5 years. Pancreatic masses were the most common tissue type with pancreatic ductal adenocarcinoma being the most common diagnosis. The number of actuations did not affect the ability to obtain a diagnosis. Tissue obtained from all three pass types were considered adequate for evaluation by pathologists. Passes with 10, 20 or 30 actuations provided equal yield in pathologic diagnosis. The 1st pass was diagnostic in all 32 cases. Our data suggest that lower number of actuations can reliably be used during tissue acquisition for EUS guided FNA without affecting the diagnostic yield. Fewer actuations may lower the risk of procedural complications including bleeding and seeding. None
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".