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Record W4313190381 · doi:10.22374/cjgim.v17i1.562

Gastric adenocarcinoma presenting as bloody ascites

2022· article· en· W4313190381 on OpenAlexaffvenue
Felix Zhou, Ari Morgenthau, Thomas Arnason, Allen Tran

Bibliographic record

VenueCanadian Journal of General Internal Medicine · 2022
Typearticle
Languageen
FieldMedicine
TopicGastrointestinal disorders and treatments
Canadian institutionsDalhousie University
Fundersnot available
KeywordsMedicineBloodyAscitesParacentesisMalignancyCirrhosisAdenocarcinomaBile ductGastroenterologySurgeryInternal medicineCancer

Abstract

fetched live from OpenAlex

A 71-year-old male presented to hospital with 3 months of increasing abdominal distention and pain. CT showed large volume ascites and gastric wall thickening in the antrum. He had no history of significant alcohol use or other risk factors for cirrhosis. He underwent paracentesis, and 3 litres of homogenously bloody ascites fluid was removed. Ascites cytology showed discohesive malignant cells. Upper endoscopy showed a 10 cm circumferential gastric mass. Biopsies revealed a diagnosis of gastric adenocarcinoma. The presence of homogenously bloody ascites can be a startling finding to healthcare providers. The differential diagnosis for bloody ascites includes hepatocellular carcinoma or other malignancy, hemorrhagic pancreatitis, perforated ulcers/varices, blunt trauma, and iatrogenic (suggested by recent paracentesis, transjuglar intrahepatic portosystemic shunt, or other procedure). The presence of bloody ascites in an otherwise relatively asymptomatic individual should prompt a search for malignancy. This case highlights a rare presentation of gastric adenocarcinoma as bloody ascites.

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)0.002
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: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0030.002
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 head0.019
GPT teacher head0.265
Teacher spread0.246 · 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

Citations0
Published2022
Admission routes2
Has abstractyes

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