Assessment of the Relationship between Clinical Variants of Psoriasis and <i>Killer Immunoglobulin-like Receptor</i> (<i>KIR</i>) Genes: A Systematic Review with Meta-analysis
Bibliographic record
Abstract
Background Psoriasis (Ps) is an autoimmune dermatosis. Previous studies have shown an association between KIR genes and susceptibility to some clinical variants of Ps. Therefore, we conducted an exhaustive systematic review with meta-analysis to evaluate the relationship between KIR genes and susceptibility to clinical variants of Ps in the overall population and according to ethnicity.Methods According to PRISMA guidelines, we performed a systematic review through PubMed and Web of Science to identify relevant available scientific publications about KIR genes and Ps. The quality of the studies was evaluated using the Newcastle–Ottawa scale. Odds ratios (OR) and 95% confidence intervals (95%CI) were estimated using random and fixed effect models for the analyzed genes. Heterogeneity was tested using Cochran’s Q-Statistic and I2, and the risk of bias was tested using the Begg test and Egger linear regression.Results A total of 10 case-control studies were included, comprising a variable number of KIR typified genes and psoriasis vulgaris (PsV) as the main clinical variant studied. In the total pooled results, the KIR2DS1 gene (OR = 1.518, p = .010, 95%CI: 1.105 to 2.086) was related to higher susceptibility to PsV, while the KIR2DS4 (OR = 0.563, p = .005, 95%CI: 0.376 to 0.842) and KIR3DL1 (OR = 0.602, p = .040, 95%CI: 0.370 to 0.977) genes were related to protection against PsV.Conclusion This meta-analysis demonstrates that subjects that carry the KIR2DS1 gene could have a potential risk factor for the development of PsV. Conversely, KIR2DS4 and 3DL1 genes appear to confer protection against PsV.
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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 | 0.017 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.045 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 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".