Are programmed cell death 1 gene polymorphisms correlated with susceptibility to rheumatoid arthritis?
Bibliographic record
Abstract
BACKGROUND: Several studies investigated the relationship between programmed cell death 1 (PDCD1) gene polymorphisms and rheumatoid arthritis (RA) risk, but the results were controversial. To explore whether PDCD1 gene polymorphisms have an effect on RA risk, we conducted this meta-analysis to investigate the relationships between PDCD1 polymorphisms (rs36084323 [PD-1.1 G/A], rs11568821 [PD-1.3 G/A] and rs2227981 [PD-1.5 C/T]) and RA risk under 4 genetic models. METHODS: PubMed, EMBASE, Web of Science, Cochrane Library China National Knowledge Infrastructure (CNKI), and Chinese Biomedical Literature Database (CBLM) were systematically searched for all eligible case-control studies. The last search was updated on September 10, 2016. Studies were accessed using Newcastle-Ottawa Scale case control study (NOS), and the combined effect size was calculated using STATA software, version 12.0. The pooled odds ratio (OR) with 95% confidence interval (CI) was calculated to assess the association. Heterogeneity analysis and subgroup analysis were also performed. Sensitivity analysis and publication bias were also performed if necessary. RESULTS: This meta-analysis included 6 studies. The result demonstrated null association between rs36084323 (PD-1.1 G/A) polymorphism and RA susceptibility in all 4 genetic models. With regard to rs11568821 (PD-1.3 G/A), statistically significant association with RA risk was observed under allele model in Caucasians (allele model A vs G, OR = 1.19, 95% CI = 1.03-1.41). There was no significant association between rs2227981 (PD-1.5 C/T) polymorphism and RA risk. CONCLUSION: The present study suggests that mutant A allele in rs11568821 (PD-1.3 G/A) might increase the susceptibility to RA in Caucasians.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.014 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".