Adverse events associated with endoscopic retrograde cholangiopancreatography: protocol for a systematic review and meta-analysis
Bibliographic record
Abstract
Introduction Endoscopic retrograde cholangiopancreatography (ERCP) is performed to diagnose and manage conditions of the biliary and pancreatic ducts. Though effective, it is associated with common adverse events (AEs). The purpose of this study is to systematically review ERCP AE rates and report up-to-date pooled estimates. Methods and analysis A comprehensive electronic search will be conducted of relevant medical databases through 10 November 2020. A study team of eight data abstracters will independently determine study eligibility, assess quality and abstract data in parallel, with any two concordant entries constituting agreement and with discrepancies resolved by consensus. The primary outcome will be the pooled incidence of post-ERCP pancreatitis, with secondary outcomes including post-ERCP bleeding, cholangitis, perforation, cholecystitis, death and unplanned healthcare encounters. Secondary outcomes will also include rates of specific and overall AEs within clinically relevant subgroups determined a priori. DerSimonian and Laird random effects models will be used to perform meta-analyses of these outcomes. Sources of heterogeneity will be explored via meta-regression. Subgroup analyses based on median dates of data collection across studies will be performed to determine whether AE rates have changed over time. Ethics and dissemination Ethics approval is not required for this study as it is a planned meta-analysis of previously published data. Participant consent is similarly not required. Dissemination is planned via presentation at relevant conferences in addition to publication in peer-reviewed journals. PROSPERO registration number CRD42020220221.
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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 | 0.059 | 0.091 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.016 | 0.024 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.055 | 0.005 |
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".