Bovine Leukemia virus (BLV) and risk of breast cancer: a systematic review and meta-analysis of case-control studies
Bibliographic record
Abstract
BACKGROUND: Breast cancer is reported as one of the most common cancers among females worldwide. Infectious agents especially viruses have been considered as role players in the development of breast cancer. Although some investigations suggest an association between bovine leukemia virus (BLV) and breast cancer, the involvement of this virus as a risk factor remains controversial. The present study aimed to find out any possible association between BLV and breast cancer through conducting a systematic review and meta-analysis. METHODS: Systematic literature search was performed by finding related case-control articles from the PubMed, Google Scholar, Web of Science, Scopus, and EMBASE databases. The heterogeneity and the multivariable-adjusted OR and corresponding 95% CI were applied by meta-analysis and forest plot across studies. All statistical analyses were performed using Stata 14.1. RESULT: Based on a comprehensive literature search, 9 case-control studies were included for meta-analysis. The combination of all included studies showed that BLV infection is associated with an increased risk of breast cancer [summary OR (95% CI) 2.57 (1.45, 4.56)]. CONCLUSION: This is the first meta-analysis to analyze a potential association between BLV infection and the risk of breast cancer. Control of the infection in cattle herds and screening of the milk and dairy products may help to reduce the transmission of the virus to humans.
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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 | 0.012 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.035 |
| Bibliometrics | 0.008 | 0.010 |
| 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) | 0.003 | 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".