Bibliographic record
Abstract
Autoinflammatory disease (AID) is a vast spectrum of disorders characterized by recurrent attacks of sterile inflammation. Since the first cloning of the familial Mediterranean fever gene in 1997, there has been a rapid rate of discovery of new AIDs. As of 2022, there have been 485 inborn errors of immunity documented by the International Union of Immunological Societies, for which many display aspects of autoinflammation. The pathophysiology of AIDs is complex. Although many are caused by rare mutations in genes that govern innate immunity, others are polygenic, where disease expression is thought to be triggered by environmental factors in genetically predisposed hosts. AIDs range in prevalence from common entities like gout to ultrarare monogenic diseases. Whereas AIDs were initially studied in pediatric populations, it is now apparent that they can present in adulthood and even in the elderly. AIDs can be clinically challenging given their rarity, as well as the heterogeneity in presentation and underlying etiology. Although the care of AIDs can span medical disciplines, the rheumatologist often plays a central role given the inflammatory nature of these illnesses. In this review, we explore the current understanding of the pathophysiology of these complex conditions and propose a classification system for AIDs. We place an emphasis on AIDs that present to the adult rheumatologist and discuss important AIDs that can mimic more classic rheumatic diseases such as systemic lupus erythematosus and inflammatory arthritis. Finally, we offer an approach to the clinical assessment, diagnosis, and management of AIDs.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".