Influence of <i>IL‐1R2</i> polymorphisms on endometrial cancer susceptibility in the Chinese Han population
Bibliographic record
Abstract
BACKGROUND: Recently, many studies have identified that genetic factor plays a crucial role in endometrial cancer development. The purpose of this study is to investigate the influence of single nucleotide polymorphisms (SNPs) of IL-1R2 on endometrial cancer susceptibility. METHODS: We performed a case-control study that included 293 patients with endometrial cancer and 579 healthy controls. Six SNPs in the IL-1R2 gene were genotyped using the Agena MassARRAY platform. Genetic models and haplotype analyses were used to assess the association between SNPs and endometrial cancer risk by computing odds ratios (ORs) and 95% confidence intervals (CIs). RESULTS: Overall analysis results found that two SNPs (rs4851527 and rs3218896) and haplotypes TGTC and TACT were significantly associated with endometrial cancer risk. Stratified analysis by age showed that rs2072472 was associated with endometrial cancer risk in age >54 subgroup. CONCLUSIONS: These findings suggested that IL-1R2 polymorphisms may contribute to the development of endometrial cancer. Further studies are required to confirm the results.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".