Association of TLR2 and TLR9 gene polymorphisms with atopic dermatitis: a systematic review and meta-analysis with trial sequential analysis
Bibliographic record
Abstract
Atopic dermatitis (AD) is a chronic, inflammatory skin disease. The mechanism was complex. Genetic mutations of Toll-like receptor (TLR) may be associated with AD, yet still unclear. We aim to provide specific evidence of the association of TLR2, TLR9 gene polymorphisms with AD. Publications were selected according to the criteria. Newcastle-Ottawa Scale was applied to evaluate the quality. The value of ORs and 95%CIs were applied to measure the associations. According to the heterogeneity, the effects model of fixed or random was selected in data combination. For TLR2 gene rs5743708 polymorphism, under allele and recessive contrasts, the pooled data showed a significant correlation, which was A vs a, OR = 0.51 (95%CI: 0.30, 0.86); AA vs Aa + aa, OR = 0.54 (95%CI: 0.33, 0.88). For TLR2 gene rs4696480 polymorphism, under allele, homozygous, heterozygous, and dominant contrasts, the pooled data showed a significant correlation, which was A vs a, OR = 0.79 (95%CI: 0.64, 0.97), AA vs aa, OR = 0.65 (95%CI: 0.43, 0.97), Aa vs aa, OR = 0.68 (95%CI: 0.48, 0.97), AA + Aa vs aa, OR = 0.67 (95%CI: 0.49, 0.93). There are significant associations of TLR2 gene rs5743708, rs4696480 polymorphisms with atopic dermatitis, while no associations are found in TLR9 gene rs5743836, rs187084 polymorphisms.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.031 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".