Role of Myositis-specific Autoantibodies in Personalized Therapy
Bibliographic record
Abstract
In a landmark breakthrough in 1976, Reichlin and Mattioli discovered the first myositis-specific antibody (MSA) called anti-Mi2 antibody that identified a specific clinical phenotype characterized by pathognomonic skin rash of dermatomyositis, typical proximal muscle weakness, good response to treatment, and the absence of interstitial lung disease and cancer. The discovery firmly placed inflammatory muscle diseases among the group of systemic rheumatic autoimmune diseases. Over the next four decades, a large number of additional MSAs have been discovered in this group of patients called “idiopathic inflammatory myopathy” (IIM). It is becoming clear that the increasing numbers of autoantibodies being discovered may necessitate a name change to “autoimmune myositis” (AIM), as recently suggested by a French-Canadian group. In the light of these discoveries, it was evident that a new classification system based on the combination of clinical phenotypes and the associated autoantibodies would soon be propounded. Preliminary report on such a classification was published in 2016 by the Swedish Group from Karolinska Institute led by Prof. Ingrid Lundberg. In October 2017, the European League Against Rheumatism and the American College of Rheumatology published the provisional classification criteria for IIM with the aim to categorize patients in uniform subgroups of clinical phenotype for meaningful drug trials. These are exciting times for clinicians, for research scientists, and for the patients with inflammatory myositis with reasons to be optimistic about a bright future. This short review provides a summary of the present knowledge with emphasis on its clinical implications.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".