Bibliographic record
Abstract
Introduction: Ankylosing spondylitis (AS) is a chronic inflammatory arthritis and represents the most common seronegative spondyloarthropathy with an approximate prevalence of one in one thousand Caucasians. Clinically, AS commonly presents as a predominantly axial arthritis affecting the spine and girdle joints. AS is a complex genetic disease with multiple susceptibility factors. Though it is known AS susceptibility has a strong genetic component, with a sibling recurrence risk (λs) of 82, the majority of the susceptibility genes are unidentified. -- Objective: The objective of this study was to identify and test potential candidate genes for susceptibility to AS in the Newfoundland population. Candidate genes were chosen based on functional relevance to disease phenotype or on positional proximity to known chromosome regions of linkage to AS. -- Methods: All patients with AS met the modified New York Criteria and the controls were ethnically matched. A Newfoundland cohort of 101 AS patients and 103 ethnically matched controls were genotyped using the Sequenom MassArray platform. All controls met Hardy Weinberg equilibrium. Analysis was performed using appropriate statistical tests. -- Results: The minor allele frequencies for the candidate genes TLR4 Asp299Gly (Pc=0.05) and TNFα promoter polymorphisms -308 (Pc=0.008), -863/-1031 (in linkage disequilibrium Pc=0.000) were found to be associated with AS. Other candidate genes tested showed no association with disease. Minor alleles of the single nucleotide polymorphisms chosen for the candidate genes FGFR2, TCIRG1, and DLL3 were not present in the Newfoundland population. No secondary associations of phenotype and genotype were noted for disease severity indices, gender or age of onset. -- Conclusion: A novel association was noted between TLR4 and AS and the association of promoter polymorphisms of TNFα was validated in the Newfoundland population.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".