BOVINE LEUKAEMIA VIRUS IN MILK SAMPLES: A SYSTEMATIC REVIEW AND META-ANALYSIS
Bibliographic record
Abstract
Bovine leukaemia virus (BLV), a relative of viruses such as HTLV-1 and -2, is a leading causative agent of enzootic bovine leucosis (EBL). This affects both dairy and beef cattle worldwide. It's essential to understand the BLV prevalence in milk for reasons spanning livestock health, food safety, and international trade. This systematic review and meta-analysis sought to determine the prevalence of BLV in bovine milk based on current primary research. A literature search was carried out across several databases, including PubMed, EMBASE, and Web of Science up to 3 October 2023 identify primary research reporting BLV counts in milk samples. The modified Newcastle-Ottawa scale was employed to gauge the quality of the incorporated studies. We conducted the meta-analysis using a random effects model. All statistical evaluations were carried out using R software, version 4.2. From an aggregate of 14,472 samples taken from 7 distinct studies, we determined a pooled BLV prevalence of 32% (95% CI: 0.23 – 0.42) in milk samples of dairy cow. However, there was significant heterogeneity, as indicated by an I² value of 99%. Sensitivity analyses revealed that specific studies exerted a considerable influence on the overall results. Utilizing both Doi plots and the LFK index, no significant publication bias was detected. The considerable presence of BLV in milk samples underscores necessitates further extensive research to grasp its broader implications and potential risks in animal and milk consumer health.
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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 | 0.018 | 0.042 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.043 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".