IgG4-associated orbital and ocular inflammation
Bibliographic record
Abstract
BACKGROUND: IgG4-associated orbital and ocular inflammation is a relatively unknown entity characterized by sclerosing inflammation with infiltration of IgG4-positive plasma cells. Some so-called idiopathic inflammation syndromes are being re-classified as IgG4-associated inflammation with histopathologic evaluation. FINDINGS: We report three cases with differing manifestations of IgG4-associated ocular and orbital inflammation: a case of recurrent, treatment-refractory sclero-uveitis that was diagnosed as granulomatosis with polyangiitis with an IgG4-related component, a case of pachymeningitis with optic neuritis that resulted in permanent visual loss, and a case of orbital inflammatory pseudotumor. All three would have been incompletely diagnosed without thorough histopathologic evaluation (including immunohistochemistry). CONCLUSIONS: IgG4-associated disease is an idiopathic, multi-organ inflammatory state that can manifest as chronic, relapsing, sclerosing inflammation in virtually any organ system. There is a wide range of presentations in ocular and orbital inflammation. Ophthalmologists should keep IgG4-associated inflammation in mind when examining chronic, sclerofibrosing inflammation with multi-system involvement. The histology of biopsy specimens is crucial in making the correct diagnosis. Timely assessment may lead to fewer diagnostic tests and more targeted therapy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".