Polyomaviruses and the risk of breast cancer: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Breast cancer is a major global health problem worldwide, affecting more than 2.25 million women annually. The disease is influenced by various factors, including some viruses, gender, age, and family history. This study aimed to conducting a comprehensive systematic review and meta-analysis of existing studies on the polyomaviruses in breast cancer. METHODS: This systematic review and meta-analysis aimed to provide an evidence-based analysis of the relationship between polyomaviruses and breast cancer. The global online databases were used to identify relevant studies published from 2000 to July 2024. The quality of each article was assessed using the Newcastle-Ottawa Scale (NOS) checklist. Data analysis was performed using STATA software, and standard errors of prevalence were calculated using the binomial distribution formula. Heterogeneity of study results was evaluated using the I-square and Q index, while publication bias was examined using the Begg's test. A random effects model was used to determine prevalence rates, and a forest plot diagram was used to present results with 95% confidence intervals. The Trim and Fill test was applied to estimate publication bias, and sensitivity analysis was performed to assess the influence of individual studies on the overall estimate. RESULTS: Nine studies met the inclusion and exclusion criteria for this analysis. In this study, the prevalence of BKV, JCV, HPyV7, KIV, WUV, SV40, and TSV in breast cancer patients was found to be 0%. By combining the results of these studies, the prevalence of PyV, MCV, and HPyV6 in breast cancer patients was 11%, 4%, and 1%, respectively. CONCLUSION: The meta-analysis presented here provides an exhaustive overview of the current literature on the prevalence of polyomaviruses in breast cancer patients. Findings indicate a potentially stronger association between PyV and breast cancer than other human polyomaviruses.
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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 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.038 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| 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".