Lack of Association Between <i>STAT4</i> Gene Polymorphism and Biopsy-proven Giant Cell Arteritis
Bibliographic record
Abstract
OBJECTIVE: To investigate the potential implication of the STAT4 gene polymorphism rs7574865 in the predisposition to or the clinical expression of giant cell arteritis (GCA). METHODS: A total of 212 patients diagnosed with biopsy-proven GCA were studied. DNA from patients and controls matched by age, sex, and ethnicity was obtained from peripheral blood. Samples were genotyped for STAT4 rs7574865 polymorphism. RESULTS: No statistically significant differences in the allele frequencies for the STAT4 rs7574865 polymorphism were observed between patients and controls. Although we observed an increased frequency of the T/T genotype in GCA patients (6.0%) compared to healthy controls (3.9%), this difference did not achieve statistical significance (OR 1.57, 95% CI 0.72-3.41). No statistically significant differences in allele or genotype frequencies were observed when patients were stratified according to the presence of typical disease features such as polymyalgia rheumatica, severe ischemic manifestations, and visual ischemic complications in the setting of this vasculitis. CONCLUSION: Our results do not support a major role of the STAT4 rs7574865 gene polymorphism in susceptibility to or clinical manifestations 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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.006 | 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".