Interleukin-1β and Interleukin-1 Receptor Antagonist Gene Polymorphisms and Gastric Cancer: A Meta-analysis
Bibliographic record
Abstract
BACKGROUND: Polymorphisms of interleukin-1B (IL1B) and its receptor antagonist (IL1RN) genes have been inconsistently associated with gastric cancer risk. We examined these associations by performing meta-analyses. MATERIALS AND METHODS: Twenty-five studies testing the association between IL1B and/or IL1RN gene polymorphisms and gastric cancer were examined: 14 studies of IL1B-511, 14 studies of IL1B-31, 8 studies of IL1B+3954, and 23 studies of IL1RN. Overall and ethnicity-specific summary odds ratios and corresponding 95% confidence intervals for gastric cancer associated with these polymorphisms were estimated using fixed- and random-effects models. Heterogeneity and publication bias were evaluated. RESULTS: IL1B-511T and IL1RN*2 were associated with gastric cancer risk in Caucasians, but not in Asians. For IL1B-511T, the association in Caucasians was stronger when intestinal-subtype and noncardia gastric cancer cases were examined. A nonsignificant trend was observed between IL1B-31C and gastric cancer in Caucasians. No significant association of IL1B+3954T and gastric cancer risk was detected. Studies with better methodologic characteristics reported stronger effects. There was no evidence of publication bias. CONCLUSION: IL1B-511T is associated with gastric cancer susceptibility in Caucasians. The meta-analyses suggest that the conflicting results among studies may be explained by variation in allele frequencies among the ethnic groups and variation in tumor types, as well as by the methodologic quality of the studies.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.046 |
| Bibliometrics | 0.006 | 0.008 |
| 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".