Endoscopic management for gastrointestinal leaks, perforations, and fistulae: Technical tips and outcomes
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".