A Challenging Diagnosis of IgG4-Related Disease When Understanding Limitations of Laboratory Testing Was Pivotal
Bibliographic record
Abstract
A 76-year-old man was incidentally found on a CT scan to have lymphadenopathy and bilateral kidney enlargement suggestive of infiltrative renal disease. He was largely asymptomatic but had bilateral salivary and lacrimal gland enlargement. A grossly elevated serum IgG (>70 g/L) with concomitant suppression of other immunoglobulins, a small IgG restriction, and a parotid biopsy revealing lymphoplasmacytic infiltrate with slight kappa light chain excess all suggested a lymphoproliferative disorder (LPD). The diagnostic workup was further confounded by a normal serum IgG4 concentration. Moreover, bone marrow and renal biopsies did not reveal evidence of LPD. Discussion with the laboratory not only clarified that the markedly increased total IgG could not be accounted for by the small IgG restriction, but also identified a discrepancy in the IgG4 measurement. Repeat analysis of a follow-up sample revealed an elevated IgG4 of 5.94 (reference interval: 0.039-0.864) g/L, which prompted a repeat parotid biopsy that showed predominant IgG4+ lymphocytic infiltrates. Despite the deluding presentations, a final diagnosis of IgG4-related disease (IgG4-RD) was made based on elevated serum IgG4 concentrations and histopathological findings. This case highlights the importance of recognizing limitations of laboratory testing and the benefit of close communications among clinical subspecialties and the laboratory.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".