The genetic basis of ankylosing spondylitis: new insights into disease pathogenesis
Bibliographic record
Abstract
Ankylosing spondylitis (AS) is a complex disease involving multiple risk factors, both genetic and environmental. AS patients are predominantly young men, and the disease is characterized by inflammation and ankylosis, mainly at the cartilage-bone interface and enthesis. HLA-B27 has been known to be the major AS-susceptibility gene for more than 40 years. Despite advances made in the past few years, progress in the search for non-human leukocyte antigen susceptibility genes has been hampered by the heterogeneity of the disease. Compared to other complex diseases, such as inflammatory bowel disease (IBD), fewer susceptibility loci have been identified in AS. Furthermore, non-major histocompatibility-complex susceptibility loci discovered, such as ERAP1 and IL23R, are likely contributors to joint inflammation. Identification and confirmation of functional variants remains a significant challenge of investigations involving genome-wide association studies (GWAS). It remains unclear why none of the AS-susceptibility genes identified in GWAS appear to be directly involved in the ankylosing process. Numerous reviews have recently been published on the genetics of AS. Therefore, aside from a brief summary of what AS GWAS has successfully achieved thus far, this review will focus on directions that could address unanswered questions raised by GWAS.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".