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Record W4313521137 · doi:10.14740/jh1049

Synchronous Presentation of Autoimmune Hepatitis and Multiple Myeloma

2022· article· en· W4313521137 on OpenAlexvenueno aff
Binoy Yohannan, Allen Omo-Ogboi, Varaha S. Tammisetti, Adan Rios

Bibliographic record

VenueJournal of Hematology · 2022
Typearticle
Languageen
FieldMedicine
TopicLiver Diseases and Immunity
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineAutoimmune hepatitisSerum protein electrophoresisPrednisoneMultiple myelomaAzathioprineLiver biopsyGastroenterologyCyclophosphamidePathologyInternal medicineBiopsyHepatitisImmunologyDiseaseMonoclonalAntibodyChemotherapy

Abstract

fetched live from OpenAlex

Autoimmune hepatitis (AIH) is a rare immune-mediated disease predominantly seen in women and triggered by various environmental factors. Rarely, AIH can be triggered by an underlying malignancy. We report a woman in her 60s who presented with markedly abnormal liver biochemical tests. Serology was positive for anti-smooth muscle antibodies and a liver biopsy confirmed AIH. During the hospital course, she developed sepsis and acute renal failure requiring dialysis support. Serum protein electrophoresis (SPEP) showed a monoclonal IgG kappa protein of 1.92 g/dL and a bone marrow biopsy revealed 7% clonal plasma cells. She had lytic lesions on skeletal survey confirming the diagnosis of a coexisting multiple myeloma (MM). Given her markedly abnormal liver chemistries, we decided to treat the AIH first and use the steroids (an important anti-myeloma therapy) as a bridge to the specific treatment of the MM once her clinical condition improved. She was treated with oral prednisone and azathioprine for AIH. One month later, a marked improvement in liver biochemical test results was noted and she was started on oral ixazomib, lenalidomide and dexamethasone. She received palliative radiotherapy to the lumbar spine (L2), left femur, and ischium lesions. This case highlights a rare co-occurrence of AIH and MM, the underlying mechanism of which is unknown.

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 score0.007
Threshold uncertainty score0.347

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 head0.011
GPT teacher head0.263
Teacher spread0.252 · 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
Published2022
Admission routes1
Has abstractyes

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