Human leukocyte antigen in Japanese patients with idiopathic inflammatory myopathy
Bibliographic record
Abstract
Objective: The human leukocyte antigen (HLA) is the strongest genetic risk factor for idiopathic inflammatory myopathy (IIM), and different HLA alleles have been reported to be associated with IIM susceptibility among different ethnic groups. In this study, we have investigated HLA alleles associated with IIM in Japanese patients. Methods: Genotyping of HLA-DRB1 and DPB1 were performed in 252 Japanese IIM patients (166 dermatomyositis [DM] and 86 polymyositis [PM] patients) and the association was analyzed with comparison to controls (n = 1026 for DRB1 and n = 413 for DPB1). Results:DRB1*08:03 was associated with IIM (p = 1.60 × 10−5, pc = .0005, odds ratio [OR] 2.11, 95% confidence interval [CI] 1.52–2.92) and DM (p = .0004, pc = .0128, OR 2.06, 95%CI 1.40–3.02). DPB1*05:01 was also associated with IIM (p = .0001, pc = .0021, OR 1.96, 95%CI 1.38–2.77) and DM (p = .0005, pc = .0075, OR 2.05, 95%CI 1.37–3.08). DRB1*09:01 (p = .0012, pc = .0368, OR 0.35, 95% CI 0.18–0.69) and DPB1*04:01(p = .0004, pc = .0057, OR 0.05, 95% CI 0.00–0.85) were protectively associated with PM. Two locus analyses suggested that DRB1*09:01 and DPB1*04:01 were independently associated with PM. Conclusion: Protective associations of HLA were detected in Japanese PM patients.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".