Systemic review and meta‐analysis of the association between interleukin‐10 and Takayasu arteritis
Bibliographic record
Abstract
OBJECTIVE: The aim of this meta-analysis is to investigate the relationship between interleukin (IL)-10 levels and its polymorphism and Takayasu arteritis (TAK). METHODS: Five databases including PubMed, Web of Science, Ovid, Sinomed and China National Knowledge Infrastructure (CNKI) were gone through from inception to March 31, 2022. Studies were screened according to the inclusion and exclusion criteria. Newcastle-Ottawa Scale (NOS) was applied to assess study quality. Strengths of association were evaluated by odds ratio (OR) and 95% CI. The T v. t (allele contrast), TT v. tt (homozygous contrast), Tt vs tt (heterozygous contrast), TT + Tt vs tt (dominant contrast) and TT vs Tt + tt (recessive contrast) models were adopted. RESULTS: Seven studies were included. No significant relationship between IL-10 and TAK was detected in the included patients (P > 0.05). The levels of IL-10 were lower in the active group than those in the stable group, which was -0.47 (95% CI: -0.93, 0.00) (P = 0.05). No significant relationships between IL-10 and TAK were found under all contrasts for polymorphisms rs1800871, rs1800872 and rs1800896 (P > 0.05). CONCLUSIONS: There was no significant difference in IL-10 levels between TAK patients and control subjects. The levels of IL-10 were lower in TAK patients in the active stage. There was no significant association between IL-10 gene polymorphisms and TAK. Further well-designed studies with larger sample sizes in patients with different stages are needed.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.029 |
| Bibliometrics | 0.006 | 0.007 |
| 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) | 0.004 | 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".