IgG4-related intracranial disease
Bibliographic record
Abstract
IgG4-related disease (IgG4-RD) is a multi-organ chronic inflammatory process caused by infiltration of IgG4-positive plasma cells in one or more organs. Intracranial involvement has only recently become better recognized. Our case series adds to the growing literature on the varying presentations of intracranial IgG4 by describing the clinical and imaging findings of three patients who presented to our institution with intracranial involvement. Our first patient presented with a mass-forming IgG4 pachymeningitis mimicking a sphenoid wing meningioma, which is to our knowledge the largest mass-forming pachymeningitis published in the literature. Our second case depicts another presentation of extensive IgG4 pachymeningitis involving both cavernous sinuses and surrounding Meckel's caves. The third case describes a patient with presumed lymphocytic hypophysitis, which was later determined to be IgG4-related hypophysitis with concomitant pachymeningitis and perineural spread along the optic nerves. The delayed diagnoses in our cases illustrates the diagnostic challenge that clinicians face in differentiating intracranial IgG4-RD from other infiltrative diseases such as sarcoidosis, granulomatous disease, tuberculosis and lymphoma. Earlier consideration of IgG4-related hypophysitis and hypertrophic pachymeningitis in the differential diagnosis can prevent significant morbidity including unnecessary surgical intervention and organ failure secondary to extensive fibrosis.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".