Restoration of Overexpressed Variable Heavy Chain 2 Transcripts with Tumor Necrosis Factor Inhibitors in Ankylosing Spondylitis
Bibliographic record
Abstract
To the Editor: Ankylosing spondylitis (AS) is a chronic inflammatory disorder characterized by progressive and destructive arthritis of the spine and pelvis1. B cells are involved in the pathogenesis of autoimmune diseases through antibody production, cytokine release, and antibody presentation to auto-reactive T cells. In AS, the role of B cells in the pathogenesis is still incompletely understood2. It has been hypothesized that the production of high affinity monoreactive autoantibodies in autoimmune disease could arise from intrinsic abnormalities in the generation of immunoglobulin genes3. Immunoglobulin gene usage can be regarded as an important factor of pathogenesis of autoimmune diseases. Investigation of variable heavy chain (VH) gene usage is important for determining whether usage of particular gene families is distorted. Several studies have investigated the VH gene usage in various autoimmune diseases, including systemic lupus erythematosus4, myasthenia gravis5, rheumatoid arthritis (RA)6, Sjögren syndrome7, and AS8 … Address correspondence to Dr. S-C. Shim, Division of Rheumatology, Department of Medicine, Daejeon Rheumatoid and Degenerative Arthritis Center, Chungnam National University Hospital, 6 munwha-ro Jung-gu, Daejeon, South Korea. E-mail: shimsc{at}cnuh.co.kr
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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".