Association between miR-124 rs531564 and miR-100 rs1834306 polymorphisms and cervical cancer: a meta-analysis
Bibliographic record
Abstract
Aim: To explore the correlations between miR-124 rs531564 and miR-100 rs1834306 polymorphisms and cervical cancer (CC). Materials and Methods: Relevant studies were searched from the electronic databases Embase, Cochrane library, and PubMed updated to January 2017, as well as through literature tracing. Studies were selected based on strict criteria, followed by the included studies which were conducted with quality assessment using Newcastle-Ottawa Scale (NOS). With odds ratios (ORs) and corresponding 95% confidence intervals (95% CIs) as effect indicators, meta-analysis for exploring the correlations between rs531564 and rs1834306 polymorphisms and CC was performed using R 3.12 software. Using Egger’s test, publication bias was elevated for the included studies. In addition, sensitivity analysis was carried out. Results: There were a total of four eligible studies, involving 3,707 participators (including 1,592 CC patients and 2,115 healthy controls). The NOS scores of the included studies were 5-7, indicating a high quality. Meta-analysis showed that all genetic models of rs531564 were statistically significant (p < 0.05), indicating that rs531564 was associated with the occurrence of CC. Nevertheless, the situation for rs1834306 was exactly the opposite. Egger’s test for rs531564 showed no publication bias, suggesting that our results were reliable. Sensitivity analysis showed that the pooled results of rs531564 were stable in general. Conclusion: These indicated that rs531564 was correlated with the development of CC, but not rs1834306.
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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) | 0.011 | 0.050 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| 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".