Rheumatoid Arthritis Known HLA Associations are Unlikely To Be Associated With Atopic Dermatitis
Bibliographic record
Abstract
To the Editor: Individuals with atopic dermatitis (AD) frequently have illnesses such as asthma and seasonal allergies. Recent studies have revealed associations between AD and rheumatoid arthritis (RA)1,2. For example, a study from Germany showed an increased risk of RA for those with AD (risk ratio 1.72, 95% CI 1.25–2.37). The study included a genetic evaluation and was not able to demonstrate that AD and RA shared known genetic risk using a genome-wide association study (GWAS) approach1. RA has an established and strong association with HLA polymorphisms that account for ~18% of the genetic risk of seropositive RA3,4. GWAS approaches do not optimally evaluate HLA genes4,5. HLA-DRβ1 amino acid residues located at positions 11,13, 71, and 74 are found in ~80% of those who have seropositive RA and represent the receptor phenotype associated with HLA-DRβ1 allelic variation4,5. Specifically, amino acids like valine (V; OR > 4.0 favoring RA), leucine (L; OR > 2.0), or serine (S; OR < 0.40) at position 11 can have profound effects on how the HLA receptor on T cells binds with antigen, potentially influencing the pathophysiology of RA4. In this letter, we report a detailed … Address correspondence to Dr. D.J. Margolis, MD, PhD, 901 Blockley Hall, 423 Guardian Drive, Philadelphia, PA 19104, USA. Email: margo{at}pennmedicine.upenn.edu.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".