Classification, Diagnosis, and Management of Idiopathic Inflammatory Myopathies
Bibliographic record
Abstract
The detection and characterization of a large array of autoantibodies, including at least 8 different antisynthetase, anti-SRP, -200/100 (HMGCR), -Mi-2, -CADM-140 (MDA5), -SAE, -p155, -MJ (NXP-2), and -PMS1, frequently associated with distinct and well-defined clinicopathological features, allowed for significant improvement in the definition and diagnosis of idiopathic inflammatory myopathies (IIM). Classification remains difficult, with lingering divergence between the different specialties involved in IIM care, but several categories clearly stand out, including dermatomyositis (DM), overlap myositis (OM), polymyositis, necrotizing myositis, and sporadic inclusion body myositis (s-IBM). Biopsy and histological analysis remain crucial, particularly in the absence of autoantibodies, to accurately specify the diagnosis and rule out mimics such as muscular dystrophies and metabolic myopathies. Numerous infectious agents (in particular human immunodeficiency virus and human T cell lymphotrophic virus-1) and drugs (statins, tumor necrosis factor inhibitors, and proton pump inhibitors) can cause mimic IIM that must also be excluded. Pharmacological treatment, in addition to glucocorticoids and immunoglobulins, now includes mycophenolate mofetil and rituximab, which proved helpful in resistant cases, particularly rituximab in DM and OM. Exercise, initially seen as potentially deleterious, recently was shown to be efficacious and safe. IIM can thus be reasonably well controlled in most cases, although aggressive disease remains refractory to treatment, including some cases of necrotizing myopathy. Sporadic IBM still seems resistant to all medications tested to date.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".