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Record W4417343787 · doi:10.4253/wjge.v17.i12.111782

Endoscopic management for gastrointestinal leaks, perforations, and fistulae: Technical tips and outcomes

2025· article· en· W4417343787 on OpenAlexaff
Chhagan Lal Birda, Jahnvi Dhar, Naveen Kumar, Shubhra Mishra, Giuseppe Dell’Anna, Cherring Tandup, Satish Subbiah Nagaraj, Stefano Francesco Crinò, Vikramjit Mitra, Zaheer Nabi, Jayanta Samanta

Bibliographic record

VenueWorld Journal of Gastrointestinal Endoscopy · 2025
Typearticle
Languageen
FieldMedicine
TopicEsophageal and GI Pathology
Canadian institutionsPancreas Centre (Canada)
Fundersnot available
KeywordsMultidisciplinary approachEndoscopic submucosal dissectionEndoscopic mucosal resectionEndoscopyEndoscopic treatmentVariety (cybernetics)Therapeutic endoscopy

Abstract

fetched live from OpenAlex

Gastrointestinal (GI) tract defects can be classified into three distinct entities: Leak, perforation, and fistula. Each arises from different mechanisms and is managed accordingly. Leaks occur most often after surgery, while perforations arise due to flexible endoscopic maneuvers. Fistulae arise from a variety of mechanisms, including specific disease states. Endoscopic management is vital in treating such defects if the region of interest can be accessed with the appropriate endoscopic accessories. The primary goal of endoscopic therapy is to interrupt the flow of luminal contents across a GI defect. Considering the proper endoscopic approach to luminal closure, several basic principles must be considered. Outcomes are dependent on the size and exact location of the leak/fistula, as well as the viability of the surrounding tissue. Almost all complex leaks and fistulae must be approached in a multidisciplinary manner, collaborating with colleagues in nutrition, radiology, and surgery. With advances in technology, a myriad of devices and accessories are available that allow a tailored approach. In this review, we discuss these modalities, provide technical tips, and review published outcomes data regarding each approach, as well as practical considerations for the successful closure of these defects.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.096
Threshold uncertainty score0.820

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.013
GPT teacher head0.301
Teacher spread0.289 · 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.

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

Citations1
Published2025
Admission routes1
Has abstractyes

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