A205 IS REPEAT ERCP REQUIRED AFTER INITIAL ENDOSCOPIC MANAGEMENT OF POST-SURGICAL BILE LEAKS? MULTI-CENTER VALIDATION OF THE CALGARY BILE LEAK RULE
Bibliographic record
Abstract
Abstract Background The Calgary Bile Leak Rule was developed to identify patients in whom biliary stent removal via gastroscopy could be safely performed in lieu of ERCP for post-surgical bile leaks. Aims This study aimed to evaluate a Modified Calgary Bile Leak Rule (MCBLR) for a cohort of patients who underwent laparoscopic cholecystectomy complicated by bile leak. Methods This retrospective cohort study included patients who underwent ERCP for management of laparoscopic cholecystectomy-induced bile leaks between 2005 and 2017. The primary outcome was defined as the absence of persisting bile leak or other pathology on follow-up ERCP. The MCBLR includes a) normal post-surgical serum alkaline phosphatase, b) small or absent leak with no other biliary pathology on initial ERCP, and c) time between initial and follow-up ERCP was 4–8 weeks. Test performance of the prediction rule was analyzed by calculating sensitivity, specificity, positive predictive value and negative predictive value. Results 124 cases met inclusion criteria, of which 116 (94%) of bile leak cases had no leak identified during the follow-up ERCP. 8 (6.4%) had a persisting bile leak on follow-up ERCP. Bivariate analysis found no factors significantly associated with the primary outcome. The MCBLR demonstrated a sensitivity of 100% (95% CI 63% - 100%), a specificity of 35% (95% CI 26% - 44%), a positive predictive value of 10% (95% CI 4% - 18%), and a negative predictive value of 100.0% (91% to 100%). Conclusions The MCBLR demonstrated high sensitivity and negative predictive value for determining the need for repeat ERCP following endoscopic management of laparoscopic cholecystectomy-induced bile leaks. Funding Agencies None
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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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 | 0.001 |
| 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".