Influence of Polymorphisms in the <b><i>Interleukin-18</i></b> Gene on Allergic Rhinitis: A Meta-Analysis
Bibliographic record
Abstract
PURPOSE: Reported associations of interleukin-18 (IL-18) single-nucleotide polymorphisms (SNPs) with allergic rhinitis (AR) have been inconsistent, prompting a meta-analysis to obtain more precise estimates. METHODS: We synthesized data from 8 articles and examined 3 IL-18 SNPs. Two SNPs (rs360721 and rs187238), in linkage disequilibrium, were combined and termed RS1. The rs1946518 SNP was analyzed separately (termed RS2). The recessive, dominant, and codominant (multiplicative) genetic models were used to estimate ORs and 95% CIs. Subgroup analysis was ethnicity-based. Sources of heterogeneity were investigated with outlier treatment. Sensitivity analysis was used to assess robustness of the associative effects. Multiple comparisons were Holm-Bonferroni corrected. RESULTS: All significant (pa < 0.05) outcomes indicating increased risks were found in the dominant/codominant models in RS1 and RS2. Five aspects of differences marked the significant African (RS1) and overall (RS2) outcomes: (i) magnitude of effect (ORs): greater (3.01-5.15) versus less (1.20-1.47); (ii) precision of -effects (95% CIs): less (1.07-21.52) versus more (1.01-1.89); (iii) outlier treated: no versus yes; (iv) sensitivity outcomes: nonrobust versus robust (dominant model only); and (v) greater evidential strength for RS2 (pa = 0.002) compared to RS1 (pa = 0.02) rendered RS2 our core finding. These levels of statistical significance for RS1/RS2 enabled both to survive the Holm-Bonferroni correction. CONCLUSIONS: The core outcome indicating a 1.5-fold increased risk could render the IL-18 polymorphisms useful in the clinical genetics of AR. Future studies that could focus on other IL-18 SNPs may find deeper associations with AR than what we found here.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.038 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".