Emerging therapy options for IgG4-related disease
Bibliographic record
Abstract
INTRODUCTION: Awareness of IgG4-related disease (IgG4-RD) is increasing worldwide and specialists are now familiar with most of its clinical manifestations and mimickers. IgG4-RD promptly responds to glucocorticoids and repeated courses are typically used to induce and maintain remission because the disease relapses in most patients. If left untreated, it can lead to organ dysfunction, organ failure and death. Advancement in our understanding of IgG4-RD pathogenesis is leading to the identification of novel therapeutic targets and emerging treatments are now setting the stage for personalized therapies for the future. AREAS COVERED: This review focuses on emerging treatment options for IgG4-RD based on our advancing understanding of disease pathophysiology. Research was performed in the English literature on Pubmed and clinicaltrials.gov databases. EXPERT OPINION: Glucocorticoids remain the first-line induction treatment for the multi-organ manifestations of IgG4-RD. Alternative immunosuppressive agents for maintaining remission are warranted in order to avoid long-term steroid toxicity, and to offer a more mechanistic and personalized therapeutic strategy. Targeting B and T-lymphocyte activation represents the most promising approach, but randomized controlled trials are eagerly awaited to confirm positive preliminary experiences reported in case series and small cohort studies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".