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Record W2312524762 · doi:10.4238/2015.march.31.21

Association of the IL-4R Q576R polymorphism and asthma in the Chinese Han population: A meta-analysis

2015· review· en· W2312524762 on OpenAlexaff
Zhiying Huang, Bo Cheng, Gaojun Cai, B.F. Zhang

Bibliographic record

VenueGenetics and Molecular Research · 2015
Typereview
Languageen
FieldMedicine
TopicAsthma and respiratory diseases
Canadian institutionsMcMaster University
Fundersnot available
KeywordsAsthmaSubgroup analysisMeta-analysisAlleleMedicinePolymorphism (computer science)Han chineseInternal medicineGenetic modelPopulationImmunologyDemographyGenotypeGeneticsSingle-nucleotide polymorphismBiologyGeneEnvironmental health

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.887
Threshold uncertainty score0.359

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.112
GPT teacher head0.429
Teacher spread0.317 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations5
Published2015
Admission routes1
Has abstractyes

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