Relationship between the rs333 Polymorphism in the CC Chemokine Receptor Type Five (CCR5) Gene and Immunological Disorders: Data from a Meta-Analysis
Bibliographic record
Abstract
Introduction: Inflammatory Bowel Disease (IBD), periodontitis and Systemic Lupus Erythematous (SLE) are multifactorial diseases, one of the factors in the course of these diseases is the rs333 polymorphism in the CC chemokine receptor type five (CCR5) gene. However, the results remain contradictory. Therefore, we aimed to perform a meta-analysis evaluating the relation between this polymorphism and the aforementioned conditions. Material and Methods: A search in the literature was performed in diverse scientific and medical databases for studies published before June 22, 2020. The data were extracted from the studies and the statistical evaluation was performed by the calculations of statistical heterogeneity (I²), Odds Ratio (OR) with 95% of Confidence Intervals (CI) and publication bias. The values of P<0.05 were considered as significant for all calculations. Results: 19 articles with 21 case/control studies in 4,304 case patients and 3,492 controls were included. The meta-analysis showed a non-significant association among the rs333 polymorphism and IBD (OR = 1.05, 95% CI: 0.91-1.20, P = 0.51), periodontitis (OR = 0.86, 95% CI: 0.64-1.17, P = 0.34) or SLE (OR = 1.00, 95% CI: 0.56-1.80, P = 1.00) under the allelic model or for any other performed calculation. There were no obvious publication bias in the analyses. Conclusion: In conclusion, this current meta-analysis evidenced the non-significant relation among the rs333 polymorphism and the risk of IBD, periodontitis or SLE. Further studies are required to validate our data.
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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 | 0.015 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.054 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| 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".