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Record W4416806467 · doi:10.1055/a-2757-3509

Correction: Number, depth, and location of inadvertent pancreatic guidewire cannulations, and their association with post-ERCP pancreatitis: multicenter real-time intra-procedural data

2025· article· en· W4416806467 on OpenAlexaff
Mehul Gupta, Millie Chau, Megan Howarth, Shane Cartwright, Sara Ficaccio, Alejandra Tepox-Padrón, Yousef Alshammari, Howard Guo, Yen‐I Chen, Andrew Singh, Lawrence Hookey, Naveen Arya, Natalia Causada Calo, Samir C. Grover, Avijit Chatterjee, Peter D. Siersema, Nirav Thosani, Steven J. Heitman, Yang Lei, Suqing Li, Zhao Wu Meng, Rachid Mohamed, Christian Turbide, B. Joseph Elmunzer, Nauzer Forbes

Bibliographic record

VenueEndoscopy · 2025
Typearticle
Languageen
FieldMedicine
TopicGallbladder and Bile Duct Disorders
Canadian institutionsOttawa HospitalThe Scarborough HospitalUniversity Health NetworkUniversity of TorontoSt. Michael's HospitalOakville-Trafalgar Memorial HospitalQueen's UniversityMcGill UniversityUniversity of VictoriaUniversity of Calgary
Fundersnot available
KeywordsMulticenter studyAssociation (psychology)MEDLINEPancreasPancreatic disease

Abstract

fetched live from OpenAlex

Correction to: Number, depth, and location of inadvertent pancreatic guidewire cannulations, and their association with post-ERCP pancreatitis: multicenter real-time intra-procedural data Endoscopy eFirst DOI: 10.1055/a-2675-4322 10.1055/a-2675-4322 In the above-mentioned article the title has been corrected. The correct title is: Number, depth, and location of inadvertent pancreatic guidewire cannulations, and their association with post-ERCP pancreatitis: multicenter real-time intra-procedural data. This was corrected in the online version on November 28, 2025. Publication History Article published online: 28 November 2025 © 2025. Thieme. All rights reserved. Georg Thieme Verlag KG Oswald-Hesse-Straße 50, 70469 Stuttgart, Germany

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.092
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.050
Threshold uncertainty score0.168

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.092
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0030.004
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0040.001
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0500.017

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 head0.009
GPT teacher head0.264
Teacher spread0.255 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations0
Published2025
Admission routes1
Has abstractno

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