High Diagnostic Yield of Endoscopic Retrograde Cholangiopancreatography Brush Cytology for Indeterminate Strictures
Bibliographic record
Abstract
Background: Endoscopic retrograde cholangiopancreatography (ERCP) brush cytology is used frequently for sampling indeterminate biliary strictures. Studies have demonstrated that the diagnostic yield of brush cytology for malignant strictures is estimated to be 6%-70%. With improved diagnostic tools, sampling techniques and specimen processing, the yield of ERCP brush cytology may be higher. This study aimed to assess the yield of brush cytology and determine factors associated with a positive diagnosis. Methods: This was a cohort study of patients who underwent ERCP brush cytology from October 2017 to May 2020. Patient demographics, clinical, procedural and pathological data were collected using chart review. Sampling data were captured up to 3 months post-index ERCP to capture repeat brushings, biopsies or surgical resections. Outcomes included the diagnostic yield, true/false positive values and true/false negative values of malignancy detection using ERCP brush cytology. Results: A total of 126 patients underwent a brush cytology, 58% were male and 79% had a stricture in the extrahepatic region. Ninety-three patients were diagnosed with a malignancy, of which 78 had positive brush cytology results and 15 had a negative brush cytology result. The diagnostic yield, sensitivity, specificity, positive predictive value, negative predictive value and accuracy were 84%, 83%, 97%, 99%, 68% and 87% respectively. Conclusion: ERCP brush cytology performed using updated sampling technique is associated with high diagnostic yield. This allows for earlier malignancy diagnosis, timely treatment and decreased need for further investigation.
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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 | 0.012 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".