IgG4‐related disease and lymphocyte‐variant hypereosinophilic syndrome: A comparative case series
Bibliographic record
Abstract
OBJECTIVE: To compare the clinical and laboratory features of IgG4-related disease (IgG4-RD) and lymphocyte-variant hypereosinophilic syndrome (L-HES), two rare diseases that often present with lymphadenopathy, gastrointestinal symptoms, eosinophilia, and elevated immunoglobulins/IgE. METHOD: Comparative case series of 31 patients with IgG4-RD and 13 patients with L-HES. RESULTS: Peripheral blood eosinophilia was present in eight of 31 patients with IgG4-RD compared to 13 of 13 patients with L-HES (median eosinophils 0.4 vs 7.0 giga/L, P=.001) and 12 of 20 patients with IgG4-RD had increased serum IgE compared to eight of 13 patients with L-HES, P=.930. Twenty-seven of 30 patients with IgG4-RD had elevated serum IgG4 compared to five of 12 patients with L-HES (median IgG4 9.6 g/L vs 0.80 g/L, P=.002). Flow cytometry demonstrated an aberrant T-cell phenotype in 7 of 23 patients with IgG4-RD and 13 of 13 patients with L-HES (P<.001). T-cell clonality by PCR was positive in 12 of 23 patients with IgG4-RD vs 10 of 13 patients with L-HES (P=.143). Patients in both groups received corticosteroids as first-line therapy. For refractory disease in IgG4-RD, rituximab was the most common steroid-sparing agent, whereas in L-HES, it was pegylated interferon-α-2a. CONCLUSION: The overlapping features of these two diseases with divergent treatment options demonstrate the importance of familiarity with both entities to optimize diagnosis and treatment.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".