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Record W61045063 · doi:10.1155/2012/754065

Jejunal Amyloidosis: A Rare Cause of Severe Gastrointestinal Bleeding

2012· article· en· W61045063 on OpenAlexaffvenue
Alan Hoi Lun Yau, Ian Scott Cornell, Justin Cheung

Bibliographic record

VenueCanadian Journal of Gastroenterology · 2012
Typearticle
Languageen
FieldMedicine
TopicPancreatitis Pathology and Treatment
Canadian institutionsUniversity of British ColumbiaRoyal Columbian Hospital
Fundersnot available
KeywordsAmyloidosisMedicineGastrointestinal bleedingGastroenterologyInternal medicineDermatology

Abstract

fetched live from OpenAlex

An 81-year-old Jamaican woman presented with melena and severe anemia, and a hemoglobin level of 47 g/L (normal range 115 g/L to 160 g/L). A gastroscopy and colonoscopy were negative for any lesions. Duodenal biopsies were normal. A subsequent computed tomography scan of the abdomen with contrast showed diffuse thickening from the jejunum to the distal small bowel and a small area of active bleeding in the mid small bowel. A technetium-99m red blood cell scan demonstrated active bleeding in the proximal jejunum. Push enteroscopy (120 cm to 150 cm below incisor) revealed mild diffuse friability starting from the proximal jejunum, as well as erosions, nodularities, polypoid protrusions and valvulae conniventes thickening in the remaining jejunum (Figures Jejunal biopsies showed extensive amyloid deposition in the lamina propria and submucosa, with Congo red stain demonstrating apple-green birefringence under polarized light (Figure Serum protein electrophoresis, immunofixation and a serum-free light chain assay confirmed the presence of monoclonal immunoglobulin G lambda paraprotein. The patient was transfused with packed red blood cells as needed (average one unit per day), and treated with bortezomib and dexamethasone for primary amyloidosis. By day 17 of hospitalization, the bleeding had resolved and the patient was subsequently discharged without any further bleeding episodes.

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)0.000
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 score0.082
Threshold uncertainty score0.517

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.020
GPT teacher head0.248
Teacher spread0.229 · 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

Citations3
Published2012
Admission routes2
Has abstractyes

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