Association of IL-10–592A/C Polymorphisms With ASCVD: A Systematic Review and Meta-analysis Protocol
Bibliographic record
Abstract
Abstract Introduction: Interleukin-10 (IL-10)-592C/A(rs1800872) polymorphism may play a role in the risk of atherosclerotic cardiovascular disease (ASCVD) such as coronary artery disease (CAD) and ischemic stroke (IS).However,the results remained controversial until now.Therefore, we will plan to reanalyze the relativity between IL-10-592C/A single nucleotide polymorphism and ASCVD in a larger clustered population. Methods and analysis: We search a comprehensive literature by performing on PubMed, Web of Science, Chinese National Knowledge Infrastructure (CNKI) databasesand and Cochrane Library for relevant articles using MeSH terms and related Entry terms (up to 14 Octobor 2022). After removing the duplicates, two reviewers will independently screen the articles for inclusion or exclusion of the study and check titles and abstracts before reading the full text.In addition,the references of the included literature will also be traced to obtain all relevant literature. Newcastle-Ottawa Scale (NOS) will be used to assess methodological quality of studies, a star-rated rating system ranging from 0 to 9 stars.Funnel plot will be used to analyze publication bias. Finally, We will perform a sensitivity analysis on the results of our meta‐analysis to estimate whether our results would be significantly affected by a single included research. Ethics and dissemination: The article uses secondary data and will not be restricted by ethical approval. The findings will be published by through publications after peers reviewed. All contributors acknowledged that any potential conflict of interest will be declared. Strengths and limitations: ①This is the first meta-analysis to explore association between IL-10-592A/C polymorphism and ASCVD. ②Gene-gene interactions and gene-environment interactions cannot be ignored, and most eligible studies do not consider these potential effects.Thus, our results may be affected to varying degrees. ③There will be a methodological heterogeneity due to different genetic testing techniques and other techniques were used between different studies
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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 | 0.051 | 0.063 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.022 | 0.019 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.072 | 0.008 |
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".