Clinical applications of intravenous immunoglobulins (IVIg) – beyond immunodeficiencies and neurology
Bibliographic record
Abstract
The clinical use of intravenous immunoglobulin (IVIg) has expanded beyond its traditional place in the treatment of patients with primary immunodeficiencies. Due to its multiple anti-inflammatory and immunomodulatory properties, IVIg is used successfully in a wide range of autoimmune and inflammatory conditions. Recognized autoimmune indications include idiopathic thrombocytopenic purpura (ITP), Kawasaki disease, Guillain-Barré syndrome and other autoimmune neuropathies, myasthenia gravis, dermatomyositis and several rare diseases. Several other indications are currently under investigation and require additional studies to establish firmly the benefit of IVIg treatment. Increasing attention is being turned to the use of IVIg in combination with other agents, such as immunosuppressive agents or monoclonal antibodies. For example, recent studies suggest that combination therapy with IVIg and rituximab (an anti-CD20 monoclonal antibody) may be effective for treatment of autoimmune mucocutaneous blistering diseases (AMBDs), with sustained clinical remission. The combination of IVIg and rituximab has also been used in the setting of organ transplantation. Firstly, IVIg +/- rituximab has been administered to highly human leucocyte antigen (HLA)-sensitized patients to reduce anti-HLA antibody levels, thereby allowing transplantation in these patients. Secondly, IVIg in combination with rituximab is effective in the treatment of antibody-mediated rejection following transplantation. Treatment with polyclonal IVIg is a promising adjunctive therapy for severe sepsis and septic shock, but its use remains controversial and further study is needed before it can be recommended routinely. This review covers new developments in these fields and highlights the broad range of potential therapeutic areas in which IVIg may have a clinical impact.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".