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Record W4407285162 · doi:10.1093/jcag/gwae059.050

A50 SUCCESSFUL HEMOSTASIS WITH THROUGH THE SCOPE CLIPS FOLLOWING OVER THE SCOPE CLIP FAILURE IN DIEULAFOY LESION

2025· article· en· W4407285162 on OpenAlexaff
S Hendis, Marek Tomaszewski

Bibliographic record

VenueJournal of the Canadian Association of Gastroenterology · 2025
Typearticle
Languageen
FieldMedicine
TopicOrgan and Tissue Transplantation Research
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsCLIPSScope (computer science)HemostasisLesionMedicineComputer scienceSurgery

Abstract

fetched live from OpenAlex

Abstract Background Dieulafoy lesions account for 1-2% of acute GI bleeding and can be difficult to treat endoscopically. Aims We present a case a 59-year-old male who presented with hematemesis, melena, and hemodynamic instability. Methods Initial esophago-gastro-duodenoscopies (EGD) did not detect a source of active bleeding. Abdominal CT angiography revealed vascular structure in the stomach’s greater curvature, indicating a Dieulafoy lesion. Embolization via interventional radiology was unsuccessful. A third EGD finally showed the bleeding as a focal ooze localized in the proximal part of the greater curvature of the stomach. We used a padlock over-the-scope clip (OTSC). Despite successful deployment and placement of the OTSC, the bleeding persisted. Results Ultimately, hemostasis was achieved using five through-the-scope clips in a zipper fashion to the mound of tissue that had been raised by the over the scope clip. We hypothesize that although the OTSC grasped the Dieulafoy lesion, the feeding vessels were not significantly compressed to achieve hemostasis. This case demonstrates the failure of an OTSC to control bleeding from a Dieulafoy lesion, which was ultimately managed with through-the-scope clips. Conclusions This underscores the importance of combining traditional and novel endoscopic techniques to achieve hemostasis. 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 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)0.000
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 score0.612
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.293
Teacher spread0.280 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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