Bibliographic record
Abstract
HLA-B27 is a major histocompatibility class I (MHC-I) molecule that plays a role in antigen presentation. It is well known for its strong association with ankylosing spondylitis (AS), a link discovered more than 40 years ago. HLA-B27 positivity in the white population carries a 20-fold increased risk of developing spondyloarthritis (SpA)1. Prevalence of AS mirrors the HLA-B27 prevalence across the world, with the highest prevalence of HLA-B27 reported in Haida Indians at 50% to Japan at 0.1%. At the time of this publication at least 132 subtypes of HLA-B27 have been identified according to the Immuno Polymorphism Database2. The prevalence of HLA-B27 in the white population is around 6% to 8%, with HLA-B*2705 being the predominant subtype. HLA-B27 is present in 85% of white patients with AS. However, less than 5% of HLA-B27–positive people ever develop AS. Apart from AS, other diseases associated with HLA-B27 are uveitis, psoriatic arthritis, and lone aortic regurgitation. Patients with HLA-B27 have a survival advantage with human immunodeficiency virus (HIV) and hepatitis C virus infection. It is thought to be due to the presence of virus-specific peptides on HLA-B27 that are recognized by protective CD8+ T cells3. There is a latitude-dependent difference in the distribution of HLA-B27, with higher latitudes including the … Address correspondence to Dr. N. Haroon, 1E-425, 399 Bathurst St., Toronto Western Hospital, Toronto M5T 2S8, Ontario, Canada. E-mail: nigil.haroon{at}uhn.ca
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".