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Record W2522627618 · doi:10.2147/ceg.s91835

Splenosis involving the gastric fundus, a rare cause of massive upper gastrointestinal bleeding: a case report and review of the literature

2016· article· en· W2522627618 on OpenAlexaff
Jason Reinglas, Kirstin Perdrizet, Stephen E. Ryan, Rakesh V. Patel

Bibliographic record

VenueClinical and Experimental Gastroenterology · 2016
Typearticle
Languageen
FieldMedicine
TopicAbdominal Trauma and Injuries
Canadian institutionsOttawa HospitalUniversity of Ottawa
Fundersnot available
KeywordsMedicineUpper gastrointestinal bleedingPathologicalBleedLaparotomyGastric fundusRadiologyAutotransplantationFundus (uterus)SplenectomySurgeryGeneral surgeryStomachPathologyEndoscopyTransplantationSpleenInternal medicine

Abstract

fetched live from OpenAlex

Splenosis, the autotransplantation of splenic tissue following splenic trauma, is uncommonly clinically significant. Splenosis is typically diagnosed incidentally on imaging or at laparotomy and has been mistakenly attributed to various malignancies and pathological conditions. On the rare occasion when splenosis plays a causative role in a pathological condition, a diagnostic challenge may ensue that can lead to a delay in both diagnosis and treatment. The following case report describes a patient presenting with a massive upper gastrointestinal bleed resulting from arterial enlargement within the gastric fundus secondary to perigastric splenosis. The cause of the bleeding was initially elusive and this case highlights the importance of a thorough clinical history when faced with a diagnostic challenge. Treatment options, including the successful use of transarterial embolization in this case, are also presented.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.004
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.042
GPT teacher head0.346
Teacher spread0.304 · 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 designCase report
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

Citations11
Published2016
Admission routes1
Has abstractyes

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