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Record W2970459962 · doi:10.4103/jnsm.jnsm_12_18

Endoscopic Ultrasound-guided Biliary Drainage: A Tailored Approach

2018· article· en· W2970459962 on OpenAlexaff
Majid Abdulrahman Almadi, Motib H AlAbdulwahhab

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2018
Typearticle
Languageen
FieldMedicine
TopicGallbladder and Bile Duct Disorders
Canadian institutionsMcGill UniversityMontreal General Hospital
Fundersnot available
KeywordsBiliary drainageEndoscopic ultrasoundRadiologyMedicineDrainageUltrasoundBiology

Abstract

fetched live from OpenAlex

Although endoscopic retrograde cholangiopancreatography (ERCP) remains the gold standard method to achieve biliary drainage (BD) in cases of obstruction, some situations preclude this option. Alternatively, percutaneous BD is associated with a number of limitations and more recently associated risks have been found to be higher than those with nonconventional interventions such as endoscopic ultrasound-guided BD (EUS-BD). EUS-BD encompasses a number of interventions and approaches depending on the underlying cause and position of obstruction. We present a case series of six cases where EUS-BD was used when ERCP was not possible or failed at a tertiary care academic center. We also describe the different types of EUS-BD based on the nomenclature that has been proposed by the Asian EUS group (AEG). In all the cases that were presented, EUS-BD was successful with no complications or adverse events. The intervention resulted in resolution of biliary obstruction and the patients underwent the planned treatment for their underlying disease. In this case series, we introduced the concept of EUS-BD to the nonspecialist and illustrated the variability of the procedure and its agile nature when applied in the right setting and with the proper expertise and the importance of the presence a complete team approach by different specialists when caring for these patients.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.415
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0130.000

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 head0.202
GPT teacher head0.522
Teacher spread0.320 · 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 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

Citations1
Published2018
Admission routes1
Has abstractyes

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