IgD Heavy-Chain Deposition Disease
Bibliographic record
Abstract
Monoclonal Ig deposition disease (MIDD) is a rare complication of monoclonal gammopathy characterized by deposition of monoclonal Ig light chains and/or heavy chains along the glomerular and tubular basement membranes. Here, we describe a unique case of IgD deposition disease. IgD deposition is difficult to diagnose, because routine immunofluorescence does not detect IgD. A 77-year-old man presented with proteinuria and renal failure, and kidney biopsy analysis showed a nodular sclerosing GN with extensive focal global glomerulosclerosis, tubular atrophy, and interstitial fibrosis. Immunofluorescence was negative for Ig deposits, although electron microscopy showed deposits in the glomeruli and along tubular basement membranes. Laser microdissection of glomeruli and mass spectrometry of extracted peptides showed a large spectra number for IgD, and immunohistochemistry showed intense glomerular and tubular staining for IgD. Together, these findings are consistent with IgD deposition disease. Bone marrow biopsy analysis showed 5% plasma cells, which stained for IgD. The patient was treated with bortezomib and dexamethasone, which resulted in improvement of hematologic parameters but no improvement of renal function. The diagnosis of IgD deposition disease underscores the value of laser microdissection and mass spectrometry in further evaluating renal biopsies when routine assessment fails to reach an accurate diagnosis.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
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".