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Record W4391886332 · doi:10.1093/jcag/gwad061.137

A137 BALLOON-ASSISTED ENDOSCOPY FOR SMALL BOWEL BLEEDING – A REVIEW

2024· review· en· W4391886332 on OpenAlexaff
Ghaus M. Malik, Jia Guo, Brendan P. Halloran, Sergio Zepeda-Gómez

Bibliographic record

VenueJournal of the Canadian Association of Gastroenterology · 2024
Typereview
Languageen
FieldMedicine
TopicGastrointestinal Bleeding Diagnosis and Treatment
Canadian institutionsUniversity of AlbertaUniversity of Calgary
Fundersnot available
KeywordsMedicineBalloonEndoscopyRadiologyGeneral surgerySurgery

Abstract

fetched live from OpenAlex

Abstract Background Gastrointestinal (GI) bleeding is a potentially life-threatening medical emergency requiring immediate medical attention. Obscure GI bleeding (OGIB) Is defined as GI bleeding with no obvious culprit lesion found on initial upper or lower endoscopy. Further investigations are needed as 75% of those cases originate in the small bowel. The advent of small bowel endoscopy, consisting of video capsule endoscopy (VCE) and device-assisted enteroscopy (DAE) has allowed clinicians to endoscopically visualize the entire GI tract and perform therapeutic interventions. These devices include balloon assisted endoscopy (BAE) and Spiral Endoscopy (SPE). Aims The objectives of this review are to analyze the definitions, etiologies, and risk factors of OGIB. Furthermore, we reviewed the diagnostic and therapeutic yields of DAE and its safety profile. Due to the adverse effects and recall of SPE, we only report here on BAE. Methods A literature review was conducted using PubMed and OvidSP (Medline) to identify articles on the use of VCE and DAE for OGIB. Keywords included a combination of gastrointestinal bleeding with occult, obscure, active and inactive, small bowel bleed, diagnostic yield, therapeutic yield, and rebleeding rate. The information was obtained via systematic review and meta-analysis, retrospective studies, and case reports. Results A total of 32 papers were reviewed. The most common causes of OGIB reported were vascular lesions (23-47%), inflammatory lesions (12-16%), and tumors (5-16%). The risk factors associated with OGIB include age, cardiac disease, liver cirrhosis, chronic kidney disease, and hereditary vascular diseases. The overall diagnostic and therapeutic yields of BAE in OGIB were 76.3% and 49.5%, respectively. Factors that increased the yield included performing an initial VCE (increase by 7%) and performing the procedure within the first 10 days after the initial bleeding episode (87.5%) as compared to after 10 days (11.1%). Recurrent bleeding can occur in up to 46% of patients. Risk factors for rebleeding include cirrhosis, overt bleeding symptoms, and presence of multiple vascular lesions. Associated adverse effects in DAE were minor in 9.1% of patients and severe in less than 2%. These include bleeding (0.8%), perforation (0.3%), and pancreatitis (0.3%). Conclusions OGIB is a challenging clinical entity. BAE now allows clinicians to diagnose and treat culprit lesions in the small bowel with reasonable success and a strong safety profile, however timing and prior VCE can further optimize this. Clinical decisions and follow up should be made based around existing risk stratification tools and scores. Further prospective research is needed to determine optimal timing of DAE from presentation of OGIB that would increase diagnostic and therapeutic yields. Figure 1. A and B) Endoscopic images of small bowel vascular lesion. C) Bleeding vascular lesion treated with hemostatic clips. Funding Agencies None

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)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.009
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0090.009
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.045
GPT teacher head0.319
Teacher spread0.274 · 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 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

Citations0
Published2024
Admission routes1
Has abstractyes

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