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Intravenous immunoglobulins induce potentially synergistic immunomodulations in autoimmune disorders

2009· review· en· W2071682030 on OpenAlexaff
Paul Imbach, Alan H. Lazarus, Thomas Kühne

Bibliographic record

VenueVox Sanguinis · 2009
Typereview
Languageen
FieldMedicine
TopicPlatelet Disorders and Treatments
Canadian institutionsCanadian Blood ServicesSt. Michael's Hospital
Fundersnot available
KeywordsMedicineImmunologyAntibodyImmune systemIntravenous ImmunoglobulinsDrugPharmacology

Abstract

fetched live from OpenAlex

The increase in platelets in patients with immune thrombocytopenia (ITP) by intravenous administration of human immunoglobulin concentrates (IVIG) reflects a therapeutic immunomodulatory intervention targeted at the disturbed immune response in many inflammatory and autoimmune disorders. These immunoglobulin concentrates contain large numbers of antibodies as well as trace levels of various other immunologically active molecules. Clinical and laboratory studies have documented various mechanisms of action of IVIG. The complex network of immunological reactions resulting from the infusion of IVIG includes changes in several cytokines, interactions with dendritic cells, T- and B- lymphocyte effects, macrophage effects, mediated by distinct Fc-gamma receptors. In addition, effects on complement components and apoptosis have also been observed. Synergism between the different elements of the immune response characterizes the beneficial effects of IVIG in inflammatory and autoimmune disorders. They have immunopathogeneses and clinical manifestations which are difficult to define and therefore IVIG treatment indications remain heterogeneous. Dose finding studies are missing for most of the indications of the drug. In future research, defining the appropriate subgroups of patients should be undertaken. This may be accomplished by prospective registries collecting data on large numbers of patients with long-term follow-up. Controlled clinical and laboratory studies may follow based on new, validated patient selection criteria and focused on mechanisms of action, leading to more evidence-based indications.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.023
GPT teacher head0.312
Teacher spread0.289 · 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 designNot applicable
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

Citations62
Published2009
Admission routes1
Has abstractyes

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