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Record W4388013926 · doi:10.1080/17474124.2023.2277776

ERCP-related adverse events: incidence, mechanisms, risk factors, prevention, and management

2023· review· en· W4388013926 on OpenAlexaff
Angelica Rivas, Simran Pherwani, Rachid Mohamed, Zachary L. Smith, B. Joseph Elmunzer, Nauzer Forbes

Bibliographic record

VenueExpert Review of Gastroenterology & Hepatology · 2023
Typereview
Languageen
FieldMedicine
TopicGallbladder and Bile Duct Disorders
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMedicineEndoscopic retrograde cholangiopancreatographyAdverse effectMEDLINEIncidence (geometry)Intensive care medicineNarrative reviewSurgeryInternal medicine

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.778
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.024
GPT teacher head0.341
Teacher spread0.317 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations24
Published2023
Admission routes1
Has abstractyes

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