Essentials in Rheumatology: Disease Management * I29. Recognition and Management of the Auto-Inflammatory Diseases
Bibliographic record
Abstract
Historically immunology was considered to be the science of self–non-self discrimination and inflammation against self was invariably considered in relationship to B and T cell tolerance failure. Indeed the classical autoantibody associated autoimmunity paradigm, that sometimes powerfully manifests as fulminant lupus-related thrombosis, for example, attests to the veracity of the autoimmunity concept that underpins much of rheumatology practice. However, non-infectious inflammation against self was suspected not to be exclusively within the realms of autoimmunity, but until recently a clearly defined concept of what exactly this represented did not exist. The teaching of immunology commences with the fact that the immune system is composed of an innate and an adaptive arm and that these are ultimately functionally integrated. Over a decade ago the word autoinflammation was used to describe rare monogenic disorders including familial mediterranean fever (FMF) and TNF-associated peroidic fever syndrome (TRAPS) where the inflammation was not onstensibly linked to autoimmunity. Recognizing that perturbation of immune responses could equally be dependent on aberrant innate immunity we defined autoinflammmation as the diametric opposite boundary to autoimmunity where disease is driven by intrinsic perturbation of the non-immune cells of the target tissues or the innate immune populations that patrol these sites [1]. The innate immune-dependent or autoinflammatory disorders have clinical phenotypes distinct from those of autoimmunity and at the molecular level often converge on key cytokines such as IL-1 or TNF. The purpose of this talk is to show how autoinflammation can be recognized based on clinical features, serology and genetics. It also shows how diseases that were formerly designated as autoimmune may in some cases be autoinflammatory at disease inception. Diseases that are intermediate between autoinflammation and autoimmunity will be briefly touched on. The proper clinical designate of autoinflammation is key for treatment strategies. Thus far, many of the autoinflammatory diseases have shown a remarkable response to strategies of IL-1 antagonism. Illustrative cases including those where a clear diagnosis was not initially evident will be used to show treatment strategies. Disclosures: Reference 1. McGonagle D, McDermott MF. A proposed classification of the immunological diseases. PLoS Med 2006;3:e297.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.020 | 0.014 |
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".