A269 THE INCREMENTAL BENEFIT OF EUS-FNA FOR DIAGNOSING MALIGNANCY AMONG INDETERMINATE EXTRAHEPATIC BILIARY STRICTURES IN ADULT PATIENTS WHO UNDERGO ERCP WITH BRUSHING CYTOLOGY
Bibliographic record
Abstract
Currently, there is no standard approach for the evaluation of indeterminate extrahepatic biliary strictures. Endoscopic retrograde cholangiopancreatography (ERCP) with brushings is the first modality of choice, however, its diagnostic yield is only 45%. Endoscopic ultrasound with fine needle aspiration (EUS-FNA) is an alternate diagnostic tool for malignant biliary strictures with a sensitivity and specificity of 80% and 97% respectively. However, its impact after ERCP is performed has not been well established. The purpose of this systematic review is to assess the current literature to determine the incremental benefit of EUS-FNA (IBEUS) for diagnosing malignancy among indeterminate extrahepatic biliary strictures in adult patients who undergo ERCP with brushing cytology. A systematic review was performed using MEDLINE, EMBASE, Cochrane, and conference proceedings from inception to July 2016. Article selection and data extraction were performed independently by two reviewers with discrepancies reviewed by a third reviewer. Pooled results were calculated using random effects model and heterogeneity was explored using stratified meta-analysis and meta-regression. The main outcome was IBEUS in the diagnosis of malignancy. Secondary analysis included biliary stricture location, use of alternative imaging, study design, and study quality score. 8 out of 3,131 citations were selected for final inclusion (study periods from 1998 to 2010). Pooled IBEUS estimate with adjustment for publication bias using the ‘trim and fill method’ was 14% (95% confidence interval, 5–23%). Heterogeneity was not significant (Q score p-value 0. 16). Among studies that considered stricture location, IBEUS may be greater for distal biliary strictures (45%) when an extrinsic mass is identified on cross-sectional imaging (29%). Estimate of effect was not influenced by whether CT or MRI were involved in the evaluation, whether EUS was performed on all patients or selectively, or study design. One in every 7 patients who underwent an EUS for an indeterminate extrahepatic biliary stricture following ERCP were diagnosed with a malignancy by EUS-FNA alone. The incremental benefit of EUS-FNA may be greater for distal strictures and when extrinsic masses are identified on cross-sectional imaging. None
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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 | 0.015 | 0.063 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.030 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".