Association Between ESR1 XBAI and Breast Cancer Susceptibility: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
PURPOSE: Estrogen receptor 1 (ESR1) XbaI polymorphisms may affect breast cancer susceptibility; however, the results of previously published studies are inconsistent. This meta-analysis aimed to investigate the relationship between ESR1 XbaI polymorphism and breast cancer risk. Methods: Articles from the PubMed, Embase, Cochrane Library, WoS, Scopus, Wanfang Data, CNKI, CBM and CQVIP databases were systematically searched to determine the association between ESR1 XbaI polymorphism and breast cancer risk. The pooled results were assessed using odds ratios (ORs) and 95% confidence intervals (CIs), followed by subgroup analysis. Results: Twenty-two studies involving 12,821 cases and 14,739 control subjects were analyzed. The pooled results indicated that ESR1 XbaI polymorphism may decrease risk of breast cancer in AG vs. AA (co-dominant model: OR = 0.88, 95% CI = 0.79-0.97, P = 0.015) and AG + GG vs. AA models (dominant model: OR = 0.89, 95% CI = 0.80-0.98, P = 0.022). Subgroup analysis indicated significant associations between the ESR1 XbaI polymorphism and breast cancer risk were observed in Asian subjects, non-Hardy-Weinberg equilibrium study, post-menopausal status and hospital-based subgroups under the AG vs. AA and AG + GG vs. AA models (all P < 0.05). Conclusions: Our analysis of pooled data indicated that AG genotype in ESR1 XbaI may be a protective factor for breast cancer patients in some subgroups.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.031 |
| Bibliometrics | 0.007 | 0.009 |
| 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) | 0.004 | 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".