Association between EHBP1 rs721048(A>G) polymorphism and prostate cancer susceptibility: a meta-analysis of 17 studies involving 150,678 subjects
Bibliographic record
Abstract
BACKGROUND: EHBP1 rs721048(A) was first identified as a prostate cancer (PCa) risk in Caucasians by genome-wide association study, but subsequent replication studies involving Caucasian and other ethnicities did not produce consistent results. The aim of this study was to obtain a more definite association between rs721048(A) and PCa risk. METHODS: We comprehensively searched several databases updated to September 2014, including PubMed, Web of Science, EBSCO, and Google Scholar. Two authors independently screened and reviewed the eligibility of each study. The quality of the included studies was assessed by the Newcastle-Ottawa scale. The association of rs721048(A) and PCa risk was assessed by pooling odds ratios (ORs) with 95% confidence intervals (CIs). RESULTS: A total of 17 studies, including 48,135 cases and 102,543 controls, published between 2008 and 2014 were included in the meta-analysis. Overall, the pooled analysis demonstrated that rs721048(A) was significantly associated with the risk of PCa under the allele model (OR=1.14, 95% CI=1.11-1.17, P=0.000). Subgroup analysis based on ethnicity revealed a significant association between rs721048(A) and PCa in Caucasian (OR=1.14, 95% CI=1.11-1.16, P=0.000), African descent (OR=1.11, 95% CI=1.01-1.23, P=0.025), and Asian (OR=1.35, 95% CI=1.12-1.64, P=0.002). CONCLUSION: Our results provided strong evidence that rs721048(A) could be a risk factor for PCa.
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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 | 0.013 | 0.020 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.044 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".