Clinical prediction rule to determine the need for repeat ERCP after endoscopic treatment of postsurgical bile leaks
Bibliographic record
Abstract
BACKGROUND AND AIMS: In patients who have undergone ERCP with biliary stenting for postsurgical bile leaks, the optimal method (ERCP or gastroscopy) and timing of stent removal is controversial. We developed a clinical prediction rule to identify cases in which a repeat ERCP is unnecessary. METHODS: Population-based study of all patients who underwent ERCP for management of surgically induced bile leaks between 2000 and 2012. Multivariate and binary recursive partitioning analyses were performed to generate a rule predicting the absence of biliary pathology on repeat endoscopic evaluation. RESULTS: A total of 259 patients were included. On multivariate analysis, postsurgical normal alkaline phosphatase (ALP; OR, 2.26; 95% CI, 1.03-4.99), time from surgery to first ERCP < 8 days (OR, 2.47; 95% CI, 1.15-5.31), and minor leak with no other pathology on initial ERCP (OR, 6.74; 95% CI, 1.75-25.89) were independently associated with the absence of persistent bile leak and other pathology on repeat ERCP. The derived rule included laparoscopic cholecystectomy, normal postsurgical ALP, minor leak with no other pathology on initial ERCP, and an interval from initial to repeat ERCP between 4 and 8 weeks. When all 4 criteria were met, the rule had a sensitivity of 94% (95% CI, 83%-99%) and a negative predictive value of 93% (95% CI, 81%-99%). Optimism-adjusted sensitivity and negative predictive value were 88% (95% CI, 76%-96%) and 86% (95% CI, 73%-96%), respectively. CONCLUSIONS: This clinical decision rule identifies patients who can have their biliary stents removed via gastroscopy, which may improve patient safety and healthcare utilization.
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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 | 0.005 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".