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.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".