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Serologic problems associated with administration of intravenous immune globulin (IVIg)

2019· article· en· W2994173447 on OpenAlexaffabout
Donald R. Branch

Bibliographic record

VenueImmunohematology · 2019
Typearticle
Languageen
FieldMedicine
TopicBlood groups and transfusion
Canadian institutionsToronto General HospitalCanadian Blood ServicesUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsMedicineAntibodyImmunologySerologyAutoimmune hemolytic anemiaAdverse effectGlobulinInternal medicine

Abstract

fetched live from OpenAlex

CONCLUSIONS: Intravenous immune globulin (IVIg) is manufactured from large pools of donor plasma and contains a high diversity of antibodies, primarily IgG. For this reason, IVIg is routinely used as antibody replacement therapy for patients having primary immunodeficiencies. In 1981, IVIg was also found to be a strong immunomodulator of various inflammatory and autoimmune conditions. This observation has led to the exponential increase in the use of IVIg throughout the world, with the United States and Canada being the biggest users of IVIg. Although relatively rare, adverse events, such as hemolytic anemia and thrombosis, can complicate the administration of IVIg. More frequently, the administration of IVIg can cause serologic challenges for the transfusion service including ABO discrepancies, positive direct antiglobulin tests, positive antibody detection tests, and incompatible crossmatches. This article will review each of the potential transfusion service challenges associated with IVIg administration.

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)0.003
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.008
GPT teacher head0.222
Teacher spread0.215 · 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 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

Citations13
Published2019
Admission routes2
Has abstractyes

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