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Record W2906777171 · doi:10.21037/hbsn.2018.12.09

Anti-donor immunoglobulin G subclass in liver transplantation

2019· review· en· W2906777171 on OpenAlexaff
Vivian C. McAlister

Bibliographic record

VenueHepatoBiliary Surgery and Nutrition · 2019
Typereview
Languageen
FieldMedicine
TopicIgG4-Related and Inflammatory Diseases
Canadian institutionsWestern University
Fundersnot available
KeywordsSubclassMedicineTransplantationLiver transplantationImmunologyTacrolimusImmune systemAntibodyInternal medicine

Abstract

fetched live from OpenAlex

Immunoglobulin G (IgG) subclasses in human health and immunity have only be sporadically studied in the half century since their discovery. Different patterns of IgG subclass production are seen if the immune response is deviated towards type 1 versus type 2. The current state of our knowledge of IgG subclasses in liver transplantation is reviewed here. While several studies have been conducted in liver disease, only four relatively small studies have been undertaken in liver transplant recipients. Total IgG4 elevation in serum is related to sclerosing pancreatico-cholangiopathy that is sensitive to treatment with steroids. Conventional immunosuppressive regimes, especially with a combination of tacrolimus and sirolimus, reduce the production of all IgG subclasses after transplantation but it is not known if they deviate the immune response. Presence of anti-donor IgG3 before transplantation, or its expansion after transplantation, has been associated with rejection and liver graft loss. Anti-GSTT1 IgG4 production after transplantation is associated with de-novo immune hepatitis. Greater knowledge of anti-donor IgG subclass responses after transplantation will allow us tailor novel treatments for greater effect.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.043
GPT teacher head0.285
Teacher spread0.243 · 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 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
Published2019
Admission routes1
Has abstractyes

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