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Record W3008224498 · doi:10.1093/jcag/gwz047.253

A254 IGA-MEDIATED WARM AUTOIMMUNE HEMOLYTIC ANEMIA IN A PATIENT WITH CROHN’S DISEASE ON VEDOLIZUMAB

2020· article· en· W3008224498 on OpenAlexaff
Matthew Cheah, Reena Khanna

Bibliographic record

VenueJournal of the Canadian Association of Gastroenterology · 2020
Typearticle
Languageen
FieldMedicine
TopicBlood groups and transfusion
Canadian institutionsWestern University
Fundersnot available
KeywordsVedolizumabMedicineAutoimmune hemolytic anemiaAnemiaCrohn's diseaseRituximabIron-deficiency anemiaGastroenterologyInternal medicineAutoimmune GastritisInflammatory bowel diseaseHemolytic anemiaImmunologyDiseaseAntibodyAutoimmunity

Abstract

fetched live from OpenAlex

Abstract Background Iron deficiency anemia and anemia of chronic disease are relatively common manifestations of Crohn’s disease. Autoimmune hemolytic anemia, however, is quite rare with few reported cases. Aims To present a rare case of IgA-mediated warm autoimmune hemolytic anemia in Crohn’s Disease. Methods A chart review and literature search were performed in preparation of this case report. Results A 21-year-old male with a recent diagnosis of Crohn’s disease on Vedolizumab presents to infusion clinic with generalized weakness, coke-colored urine and weight loss. Physical examination was remarkable for tachycardia and jaundice. Laboratory investigations revealed profound anemia with IgA-mediated DAT positivity. The patient remained admitted in hospital for a prolonged period. Bone marrow biopsy, CT imaging and infectious workup were negative. Vedolizumab was held and treatment with both high-dose corticosteroids and rituximab was required. Eventually, the anemia would stabilize and Vedolizumab was safely resumed as an outpatient. Conclusions Here we report a both rare and challenging case of IgA-mediated DAT positive autoimmune hemolytic anemia in a patient with Crohn’s disease who was successfully treated with corticosteroids and rituxumab. 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)0.000
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 score0.068
Threshold uncertainty score0.947

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
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 head0.006
GPT teacher head0.195
Teacher spread0.188 · 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
Published2020
Admission routes1
Has abstractyes

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