Role of rs1343151 <i>IL23R</i> and rs3790567 <i>IL12RB2</i> Polymorphisms in Biopsy-proven Giant Cell Arteritis
Bibliographic record
Abstract
OBJECTIVE: To assess the potential association between the rs1343151 IL23R and the rs3790567 IL12RB2 polymorphisms and giant cell arteritis (GCA). We also studied whether these polymorphisms might influence the phenotypic expression of GCA. METHODS: In total, 357 Spanish patients with biopsy-proven GCA and 574 matched controls were assessed. DNA from patients and controls was obtained from peripheral blood. Samples were genotyped for the rs1343151 IL23R and the rs3790567 IL12RB2 polymorphisms using a predesigned TaqMan allele discrimination assay and by polymerase chain reaction amplification. RESULTS: Regarding the rs1343151 IL23R polymorphism, no significant differences in the genotype or allele frequencies between GCA patients and healthy controls were observed. The frequency of the minor allele A of the rs3790567 IL12RB2 variant was increased in GCA patients compared with controls (30.1% vs 25.7%, respectively; p = 0.039, OR 1.25, 95% CI 1.01-1.54). An increased frequency of subjects carrying the minor allele A (GA+AA genotypes) of the rs3790567 IL12RB2 polymorphism was found among GCA patients compared with controls (52.8% vs 44.4%; p = 0.013, OR 1.40, 95% CI 1.06-1.85). Although a higher frequency of the combination of minor alleles (A-A) in the subgroup of patients with visual ischemic complications compared with the combination of both major alleles (G-G; p = 0.029) or with the other allelic combinations (p = 0.035) was found, logistic regression analysis showed that this association was no longer significant after adjustment for potential confounding factors (A-A vs G-G: OR 2.10, 95% CI 0.88-5.04, p = 0.096). CONCLUSION: Our results support a potential influence of the rs3790567 IL12RB2 polymorphism in the pathogenesis of GCA.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".