ERCP-related adverse events: incidence, mechanisms, risk factors, prevention, and management
Bibliographic record
Abstract
INTRODUCTION: Endoscopic retrograde cholangiopancreatography (ERCP) is a commonly performed procedure for pancreaticobiliary disease. While ERCP is highly effective, it is also associated with the highest adverse event (AE) rates of all commonly performed endoscopic procedures. Thus, it is critical that endoscopists and caregivers of patients undergoing ERCP have clear understandings of ERCP-related AEs. AREAS COVERED: This narrative review provides a comprehensive overview of the available evidence on ERCP-related AEs. For the purposes of this review, we subdivide the presentation of each ERCP-related AE according to the following clinically relevant domains: definitions and incidence, proposed mechanisms, risk factors, prevention, and recognition and management. The evidence informing this review was derived in part from a search of the electronic databases PubMed, Embase, and Cochrane, performed on 1 May 20231 May 2023. EXPERT OPINION: Knowledge of ERCP-related AEs is critical not only given potential improvements in peri-procedural quality and related care that can ensue but also given the importance of reviewing these considerations with patients during informed consent. The ERCP community and researchers should aim to apply standardized definitions of AEs. Evidence-based knowledge of ERCP risk factors should inform patient care decisions during training and beyond.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".