Serum level of IL-10 and IL-10-1082G/A polymorphism are associated with the risk of ischemic stroke: A meta-analysis
Bibliographic record
Abstract
Abstract BACKGROUND Stroke is one of the leading causes of disability and mortality among adults worldwide. The aim of the study was to confirm the relationship of serum interleukin-10 (IL-10) and its gene polymorphism with the risk of ischemic stroke (IS). METHODS PubMed and China Wanfang database were systematically searched up to September 2, 2019. Studies illustrating on the association between serum IL-10 or IL-10-1082G/A, IL-10-819C/T, IL-10- 592C/A polymorphisms and IS susceptibility were included in this study. Newcastle–Ottawa scale was used to assess the study quality. RevMan 5.3 was used for statistical analysis. RESULTS: Seventeen case-control studies were included in this meta-analysis which provides 3754 patients with IS and 5064 controls. Combined analysis indicated that patients with IS had lower serum level of IL-10 (Mean difference [MD]: -4.25; 95% confidence interval [CI]: -6.14 to -2.36, p<0.0001). An association was identified between IL-10-1082G/A polymorphism and the risk of IS, but no association was found between polymorphism of IL-10-819C/T or IL-10- 592C/A and the risk of IS when all ethnic groups were considered together. For IL-10-1082G/A polymorphism, individuals with AA-genotype might have an increased risk of IS among Chinese Han population while no such correlation was observed in other ethnic group. CONCLUSION This meta-analysis suggested that low serum level of IL-10 and IL-10-1082G/A polymorphism may be associated with the risk of IS. More clinical studies should be conducted to confirm the relationship between serum IL-10 level and the risk of IS in all ethnic groups.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.044 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".