IgG4 plasma cell myeloma without clinical evidence of IgG4-related disease: a report of two cases
Bibliographic record
Abstract
Background: Serum IgG4 is typically measured to investigate for Immunoglobulin G4-related Disease (IgG4-RD), a fibroinflammatory condition associated with polyclonal increase in serum IgG4. However, increased IgG4 can also be monoclonal, and little is known about IgG4 myeloma. Methods: We describe two cases of IgG4 myeloma without clinical, radiologic, or laboratory features of IgG4-related disease. Results: An 84 year old man presented with anemia and compression fractures and a 77 year old man presented with anemia, hypercalcemia and renal failure. Both had markedly elevated monoclonal serum IgG4, 34 g/L and 48 g/L in the beta region, and increased IgG positive bone marrow plasma cells, 50% and 80%, respectively. Neither had clinical or radiological manifestations of IgG4-related disease (IgG4-RD) such as salivary or lacrimal gland swelling, autoimmune pancreatitis , or retroperitoneal fibrosis. Both cases responded well to standard myeloma therapy. The IgG4 paraprotein caused spuriously elevated beta-2 microglobulin of 45.2 mg/L in case two due to interference with the assay. Conclusion: These cases illustrate the importance of performing serum protein electrophoresis in tandem with IgG subclasses to distinguish between polyclonal and monoclonal increases in serum IgG4. The lack of typical IgG4-RD features in these two patients suggests that monoclonal elevation in serum IgG4 alone is insufficient to cause the organ damage characteristic of IgG4-RD. Larger studies of IgG myeloma subtypes are warranted to explore whether IgG1, IgG2, IgG3 and IgG4 myeloma differ in natural history and whether the interference with beta-2 microglobulin is specific to IgG4 monoclonal proteins.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.009 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".