Autoantibodies to Mi-2 alpha and Mi-2 beta in patients with idiopathic inflammatory myopathy
Bibliographic record
Abstract
OBJECTIVES: The objective of this study was to compare the results obtained from different assays for the detection of anti-Mi-2 antibodies, which are important markers in the diagnosis of DM. METHODS: The study included 82 patients (68 females/14 males), most of whom had DM (n = 57), followed by PM (n = 16) and juvenile DM (n = 9). All samples were tested using a novel particle-based multi-analyte technology (PMAT) (Inova Diagnostics, research use only) in parallel with a line immunoassay (LIA: Euroimmun). To assess clinical specificity for the PMAT assay, a total of 775 disease and healthy controls were tested. RESULTS: 29 samples were positive by at least one test for anti-Mi-2 antibodies. Of those, 24 were Mi-2β LIA+, five were Mi-2α LIA+ and 23 Mi-2 PMAT+. The comparison shows varying agreement between the different methods (kappa 0.27-0.77). When LIA results were used as reference for receiver operating characteristics analysis, high area under the curve values were found for both PMAT vs LIA Mi-2α and LIA Mi-2β. When analysing the results in the context of the myositis phenotype, PMAT associated closest with the DM phenotype. In the control group, 3/775 controls (all low levels) were anti-Mi-2+ resulting in a sensitivity and specificity of 28.1% and 99.6%, respectively. CONCLUSION: Overall, good agreement was found between LIA and PMAT for anti-Mi-2 antibodies, which is important for the standardization of autoantibodies. Anti-Mi-2β antibodies measured by PMAT tend be more highly associated with the clinical phenotype of DM.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".