Gastrointestinal Endoscopic Ultrasound-Guided Fine-Needle Aspiration Biopsy Specimens: Adequate Diagnostic Yield and Accuracy Can Be Achieved without On-Site Evaluation
Bibliographic record
Abstract
BACKGROUND: Endoscopic ultrasound-guided fine-needle aspiration biopsy (EUS-FNA) is the preferred method for biopsying the gastrointestinal tract, and rapid on-site cytological evaluation is considered standard practice. Our institution does not perform on-site evaluation; this study analyzes our overall diagnostic yield, accuracy, and incidence of nondiagnostic cases to determine the validity of this strategy. DESIGN: Data encompassing clinical information, procedural records, and cytological assessment were analyzed for gastrointestinal EUS-FNA procedures (n = 85) performed at Vancouver General Hospital from January 2012 to January 2013. We compared our results with those of studies that had on-site evaluation and studies that did not have on-site evaluation. RESULTS: Eighty-five biopsies were performed in 78 patients, from sites that included the pancreas, the stomach, the duodenum, lymph nodes, and retroperitoneal masses. Malignancies were diagnosed in 45 (53%) biopsies, while 24 (29%) encompassed benign entities. Suspicious and atypical results were recorded in 8 (9%) and 6 (7%) cases, respectively. Only 2 (2%) cases received a cytological diagnosis of 'nondiagnostic'. Our overall accuracy was 72%, our diagnostic yield was 98%, and our nondiagnostic rate was 2%. Our results did not significantly differ from those of studies that did have on-site evaluation. CONCLUSION: Our study highlights that adequate diagnostic accuracy can be achieved without on-site evaluation.
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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 | 0.003 | 0.020 |
| 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".