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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.069 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 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".