Bibliographic record
Abstract
This article summarizes the proceedings of a one-day international workshop held in July 2009 on the role of HLA-B27 in the pathogenesis of ankylosing spondylitis (AS) and related disorders. HLA-B27 is found in about 90% of patients with AS, with an odds ratio of about 100, but the mechanism underlying this association is not known. There are currently 3 major mechanistic hypotheses for this association: (1) T cell recognition of one or more B27 presented peptides; (2) B27 heavy-chain misfolding that induces an unfolded protein response; and (3) innate immune recognition of cell-surface expressed B27 heavy-chain dimers. None of these hypotheses accounts for the tissue specificity of the inflammation characteristic of AS. These hypotheses were discussed in the context of known epidemiologic, biochemical, structural, and immunologic differences among HLA-B27 subtypes; data from the HLA-B27 transgenic rat model of spondyloarthritis; the growing list of other genes that have been found to be associated with AS; and other data on the pathogenesis of spondyloarthritis. Proposed directions for future research include expanded efforts to define similarities and differences among the B27 subtypes; further development of animal models; identifying the interactions of B27 with the products of other genes associated with AS; and continued investigation into the pathogenesis of spondyloarthritis.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 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".