Association Between <i>TNF-α</i> -308G/A Polymorphism and Risk of Immune Thrombocytopenia: A Meta-Analysis
Bibliographic record
Abstract
Objective: Previous studies have investigated the association between tumor necrosis factor-alpha (TNF-α) -308G/A polymorphism and risk of immune thrombocytopenia (ITP), but the reported results have been inconsistent. Thus, a systematic meta-analysis was performed to resolve this discrepancy. Methods: Electronic databases and the cited references of the obtained published articles were manually searched. Quality assessment of each study was conducted using the Newcastle–Ottawa Scale (NOS). All case–control studies were used to assess the strength of the association. Statistical analysis was performed using Stata version 12.0. Results: Eight high-quality studies, including 947 patients and 1911 controls, were selected for the final meta-analysis. There was no significant association between TNF-α -308G/A polymorphism and ITP in overall and Asian populations. However, a significant positive association was observed between them in the dominant genetic model (AA+AG versus GG) in the Caucasian population (OR = 1.35, 95% confidence interval [CI]: 1.07–1.71, PH = 0.173). Conclusions: Our finding suggested that TNF-α -308G/A might be involved in development of ITP in the Caucasian population, but not in the Asian population. Among Caucasians the A allele (AA+AG) was associated with ITP. However, larger-scale studies are required to confirm our findings.
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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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.053 |
| Bibliometrics | 0.006 | 0.007 |
| 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".