Does Onsite Cytotechnology Evaluation Improve the Accuracy of Endoscopic Ultrasound-Guided Fine-Needle Aspiration Biopsy?
Bibliographic record
Abstract
BACKGROUND: Endoscopic ultrasound-guided fine-needle aspiration (EUS-FNA) is the preferred modality for the cytological diagnosis of various cancers. Onsite cytopathology interpretation is not available in most centres. OBJECTIVE: To assess whether the the adequacy of tissue sampling assessed by an onsite cytotechnologist improves the diagnostic accuracy of EUS-FNA. METHODS: The present study is a retrospective review of all patients undergoing solid mass EUS-FNA between September 2005 and August 2007. Patients in group I (September 2005 to August 2006) had cytology slides prepared by an endoscopy nurse. Patients in group II (September 2006 to August 2007) had cytology slides prepared, stained and assessed for adequacy of tissue sampling by a cytotechnologist in the endoscopy suite. The final cytopathological diagnosis (definitely positive, definitely negative or inconclusive) was compared between the two groups. RESULTS: A total of 49 EUS-FNA procedures were performed in 47 patients in group I and 60 EUS-FNA procedures in 55 patients in group II. Pancreatic masses were the most common target site in both groups. The total number of needle passes was 105 in group I (mean 2.14 passes per patient; range one to five needle passes) and 158 in group II (mean 2.63 passes per patient; range one to four needle passes). The difference in the number of needle passes was not statistically significant between groups. The final diagnosis was definite in 53% in group I compared with 77% in group II (P=0.01). The percentage of inconclusive diagnoses was 47% in group I and 23% in group II (P=0.001). CONCLUSION: Onsite cytotechnologist interpretation of adequacy of tissue sampling significantly improves the diagnostic yield of EUS-FNA. This appears to be independent of the total number of needle passes undertaken for tissue sampling.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".