IgG4-related disease: what a hematologist needs to know
Bibliographic record
Abstract
IgG4-related disease is a fibro-inflammatory condition that can affect nearly any organ system. Common presentations include major salivary and lacrimal gland enlargement, orbital disease, autoimmune pancreatitis, retroperitoneal fibrosis and tubulointerstitial nephritis. This review focuses on the hematologic manifestations of IgG4-related disease, including lymphadenopathy, eosinophilia, and polyclonal hypergammaglobulinemia. The disease can easily be missed by unsuspecting hematologists, as patients may present with clinical problems that mimic disorders such as multicentric Castleman disease, lymphoma, plasma cell neoplasms and hypereosinophilic syndromes. When IgG4-related disease is suspected, serum protein electrophoresis and IgG subclasses are helpful as initial tests but a firm histological diagnosis is essential both to confirm the diagnosis and to rule out mimickers. The central histopathological features are a dense, polyclonal, lymphoplasmacytic infiltrate enriched with IgG4-positive plasma cells (with an IgG4/IgG ratio >40%), storiform fibrosis, and obliterative phlebitis. Importantly for hematologists, the latter two features are seen in all tissues except bone marrow and lymph nodes, making these two sites suboptimal for histological confirmation. Many patients follow an indolent course and respond well to treatment, but a significant proportion may have highly morbid or fatal complications such as periaortitis, severe retroperitoneal fibrosis or pachymeningitis. Corticosteroids are effective but cause new or worsening diabetes in about 40% of patients. Initial response rates to rituximab are high but durable remissions are rare. More intensive lymphoma chemotherapy regimens may be required in rare cases of severe, refractory disease, and targeted therapy against plasmablasts, IgE and other disease biomarkers warrant further exploration.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.009 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".