Association of the IL-4R Q576R polymorphism and asthma in the Chinese Han population: A meta-analysis
Bibliographic record
Abstract
The IL-4R Q576R polymorphism has been reported to increase susceptibility to asthma, but the results are controversial. Thus, we performed a meta-analysis to evaluate the association of the IL-4R Q576R polymorphism and asthma risk in the Chinese Han population. A total of sixteen eligible case-control studies that evaluated the relationship between the IL-4R Q576R polymorphism and asthma in the Chinese Han population were obtained by comprehensive literature search incorporating electronic databases, and included 2077 asthma cases and 1589 controls. Our analysis detected a significant association between the IL-4R Q576R polymorphism and the risk of asthma in the Chinese Han population (Allelic model: OR = 1.481, 95%CI = 1.134-1.935, P = 0.004; Dominant model: OR = 1.542, 95%CI = 1.194-1.990, P = 0.001; Recessive model: OR = 1.695, 95%CI = 1.170-2.456, P = 0.005, Additive model: OR = 1.897, 95%CI = 1.299-2.771, P = 0.005). The year of publication and size of total sample might be sources of between-study heterogeneity. Upon subgroup analysis by size of total sample of each study, the significant association only remained in a subgroup with a small sample size. In summary, our meta-analysis suggested that the IL-4R Q576R polymorphism is associated with asthma in the Chinese Han 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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.034 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".