Associations of <i>toll‐like receptor 4</i> and <i>2</i> gene polymorphisms with susceptibility to ankylosing spondylitis: A meta‐analysis
Bibliographic record
Abstract
BACKGROUND: The published evidences on the correlations of toll-like receptor 4 (TLR4) and TLR9 gene polymorphisms and ankylosing spondylitis (AS) were conflicting. The purpose of this study was to investigate whether TLR4 and TLR9 gene polymorphisms conferred susceptibility to AS through a meta-analysis approach. METHODS: Databases of PubMed, Web of Science, EMBASE, Cochrane Library, CNKI and Wanfang were retrieved for relevant publications up to 20 June 2020. Study quality was assessed based on Newcastle-Ottawa scale (NOS). Odds ratios (ORs) and 95% confidence intervals (95% CIs) were used to judge the associations. RESULTS: Totally, 13 articles with 3,055 AS cases and 4,238 controls were incorporated into this meta-analysis, and four most widely reported polymorphisms (TLR4-rs4986790, TLR4-rs4986791, TLR9-rs55704465 and TLR9-rs187084) were analysed. All included studies were in high quality. The pooled data did not support any significant association between the four studied polymorphisms and AS susceptibility. CONCLUSIONS: The present meta-analysis suggests there is no significant association between TLR4-rs4986790, TLR4-rs4986791, TLR9-rs55704465 and TLR9-rs187084 polymorphisms and AS.
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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 | 0.014 | 0.025 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.060 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".