Ethnicity-specific associations between the promoter region G-308A polymorphism (rs1800629) of the TNF-α gene and the development of end-stage renal disease: An evidence-based meta-analysis and trial sequential analysis
Bibliographic record
Abstract
Tumor necrosis factor-alpha (TNF-α), is partly attributed to pathogenesis of end-stage renal disease (ESRD). Inconsistency of reported associations between TNF-α G-308A polymorphism (rs1800629) and ESRD prompted a meta-analysis to obtain more precise estimates. Eleven case-control studies from 11 articles were included. Pooled odds ratios (OR) and 95% confidence intervals (95% CIs) were estimated to evaluate the association. Subgroup analysis was based on ethnicity (Caucasian and Asian). Multiple comparisons were Bonferroni-corrected. Trial sequential analysis (TSA) was implemented to ascertain the reliability of results. Sensitivity analyses and publication bias tests were performed on significant results. There were no significant association (pa >0.05) in the overall and ethnic subgroup. Indians, three significant pool ORs (pa < 0.01-0.03) showed increased susceptibility to ESRD in homozygous (OR, 6.57; 95% CI, 1.45 to 29.75; pa = 0.01), recessive (OR, 6.75; 95% CI, 1.44 to 31.56; pa = 0.02), and codominant (OR, 2.06; 95% CI, 1.08 to 3.94; pa = 0.03) models. TSA indicated the robustness of such association in the Indian population. The main outcomes were robust without evidence of publication bias. This study showed associations between TNF-α G-308A and ESRD are confined to Indians, which are susceptible to ESRD up to approximately 7 times.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.047 |
| Bibliometrics | 0.003 | 0.005 |
| 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) | 0.002 | 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".