Impact of periampullary diverticula on the rates of successful cannulation and <scp>ERCP</scp> complications: An up‐to‐date systematic review and meta‐analysis
Bibliographic record
Abstract
OBJECTIVES: Periampullary diverticulum (PAD) is usually incidentally discovered during abdominal imaging, gastrointestinal endoscopy, and endoscopic retrograde cholangiopancreatography (ERCP). The influence of PAD on ERCP outcomes is unclear. The aim of this systematic review and meta-analysis was to provide an up-to-date evaluation of the impact of PAD on cannulation and ERCP-related complications. METHODS: PubMed, Web of Science, Cochrane Library and EMBASE databases were searched for relevant articles published up to October 31, 2023. The rates of successful cannulation and post-ERCP complications were compared between the PAD and non-PAD groups. The quality of the studies was evaluated with the Newcastle-Ottawa Scale (NOS). The meta-analysis was conducted using Review Manager 5.3. RESULTS: Twenty-eight articles were included. Non-PAD was associated with a relatively high cannulation success rate (odds ratio [OR] 0.72, 95% confidence interval [CI] 0.54-0.97, p = 0.03). However, after 2015, PAD was not correlated with cannulation failure (OR 0.81, 95% CI 0.59-1.11, p = 0.20). Compared with intradiverticular papilla (IDP), non-IDP had a higher successful cannulation rate (OR 0.42, 95% CI 0.25-0.72, p = 0.002), while IDP increased the difficult cannulation rate (OR 1.60, 95% CI 1.05-2.44, p = 0.03). Additionally, PAD increased the incidence of ERCP-related pancreatitis (OR 1.24, 95% CI 1.10-1.40, p = 0.0006) and bleeding (OR 1.34, 95% CI 1.03-1.73, p = 0.03). CONCLUSIONS: Although PAD, especially IDP, decreased the cannulation success rate, PAD was no longer considered a significant obstacle to cannulation after 2015. PAD increased the incidence of post-ERCP pancreatitis and bleeding.
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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 | 0.012 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.042 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".