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Record W4399678700 · doi:10.1002/jpr3.12098

B cell depletion for autoimmune liver diseases: A retrospective review of indications and outcomes

2024· review· en· W4399678700 on OpenAlexaff
Guillermo Costaguta, Fernando Álvarez

Bibliographic record

VenueJPGN Reports · 2024
Typereview
Languageen
FieldMedicine
TopicLiver Diseases and Immunity
Canadian institutionsUniversité de MontréalCentre Hospitalier Universitaire Sainte-Justine
Fundersnot available
KeywordsMedicineRetrospective cohort studyIntensive care medicineImmunologyInternal medicine

Abstract

fetched live from OpenAlex

Abstract Objectives Pediatric autoimmune hepatitis has an incidence of 0.23/100.000 children in North America, with a bleak prognosis if left untreated. Steroids are the therapy of choice but are not always effective. B cell depletion is a safe and effective therapy that allows for a steroid‐sparing protocol, especially in patients who do not tolerate side effects. Methods We retrospectively reviewed rituximab‐treated patients between 2017 and 2022. Demographics, previous treatments, reasons for B cell depletion, response, and adverse effects were noted. Results Six patients with a mean age of 10.2 years were included. All patients had comorbidities that rendered treatment with steroids unsuccessful or undesirable. Rituximab was started at a mean follow‐up of 8 months. After 6 months, the mean alanine transaminase and aspartate transaminase levels decreased from 575 IU/L and 342 IU/L, respectively, to 28 IU/L ( p = 0.02) and 36 IU/L ( p = 0.008), respectively. Mean γ‐glutamyl transpeptidase decreased from 105 to 25 IU/L ( p = 0.01). Immunoglobulin G levels were normalized in all patients ( p = 0.01). No severe adverse events were observed. One patient had persistent hypogammaglobulinemia, and another had lymphopenia. Conclusion B‐cell depletion is an effective and safe treatment for autoimmune liver diseases and should be included as an option, particularly for relapsing patients in whom steroids are undesirable or have shown nonadherence.

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: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.765
Threshold uncertainty score0.876

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
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 head0.028
GPT teacher head0.350
Teacher spread0.322 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations5
Published2024
Admission routes1
Has abstractyes

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