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Record W2114331823 · doi:10.1111/trf.13097

The role of inflammation in intravenous immune globulin–mediated hemolysis

2015· review· en· W2114331823 on OpenAlexafffund
Jacob Pendergrast, Kezia Willie‐Ramharack, Lorna Sampson, Vinçent Laroche, Donald R. Branch

Bibliographic record

VenueTransfusion · 2015
Typereview
Languageen
FieldMedicine
TopicPlatelet Disorders and Treatments
Canadian institutionsCanadian Blood ServicesHôpital de l'Enfant-JésusUniversity Health Network
FundersGrifolsCanadian Blood Services
KeywordsHemolysisMedicineInflammationImmunologyImmune systemCytokine

Abstract

fetched live from OpenAlex

Intravenous immune globulin (IVIG) therapy has shown great success in a number of autoimmune and inflammatory conditions and its use continues to increase worldwide. There is growing awareness of significant side effects of high-dose IVIG: however, particularly severe hemolysis in patients that are non-group O. It has been proposed that IVIG-associated hemolysis may be heralded by an existing inflammatory condition. In the work presented herein, we have provided a review of the pathophysiology of inflammation, particularly as it applies in immune-mediated red blood cell hemolysis, and a summary of previous publications that suggest an association between IVIG-mediated hemolysis and a state of existing inflammation. In addition, preliminary results from a prospective study to address the mechanism of IVIG-associated hemolysis are provided. These preliminary data support the idea of an existing inflammatory condition preceding overt hemolysis after high-dose IVIG therapy that: 1) is restricted to non-group O patients, 2) is seen when using IVIG doses of more than 2 g/kg, 3) involves an activated mononuclear phagocyte system, 4) may be presaged by a significant increase in the anti-inflammatory cytokine interleukin-1 receptor agonist, and 5) is independent of secretor status.

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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.998
Threshold uncertainty score0.572

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 head0.015
GPT teacher head0.281
Teacher spread0.266 · 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 designOther design
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

Citations30
Published2015
Admission routes2
Has abstractyes

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