Genetic Markers in a Medieval Case of Ankylosing Spondylitis
Bibliographic record
Abstract
To the Editor: Ankylosing spondylitis (AS) is a chronic rheumatic autoimmune disease that affects mainly the sacroiliac joints and spine, where it causes ankylosis through processes of inflammation and ossification. Although the exact accurate etiology of AS is unknown, the disease’s development has been shown to be influenced by genetic factors. The HLA-B27 allele is the strongest genetic marker associated with AS1, but there are other genes both within and outside the MHC that are involved in the development of AS. The most significant laboratory test in the diagnosis of AS involves detecting the presence of the allele HLA-B27, because 90–95% of patients with AS have this allele2. Nevertheless, this allele is found in about 10% of the world’s population; of them, only 5% ultimately develop this disease2. This means that when there are several radiological and clinical symptoms, detecting the allele HLA-B27 is not enough to diagnose the disorder. The aim of our study was to identify several genetic markers associated with the development of AS [HLA-B27 allele and single–nucleotide polymorphisms (SNP) in interleukin 23R ( IL-23R ) and … Address correspondence to I. Martín Laza, Department of Genetics, Physical Anthropology, and Animal Physiology, UPV/EHU, Barrio Sarriena s/n 48940 Leioa, Bizkaia, Spain. E-mail: imanol.martinl{at}ehu.es
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".