Meta-analysis of the effect of oral selenium supplementation on milk selenium concentration in cattle
Bibliographic record
Abstract
Soils in many regions of the world have a low Se content. Consequently, forages and crops grown on these soils may provide inadequate dietary Se for humans and grazing animals. Selenium supplementation has been used to enhance Se status and milk Se concentration, but results conflict. Milk Se concentration appears to be a useful indicator of animal and herd Se status, and reflects the responsiveness to supplementation. A systematic review and meta-analysis were carried out to summarize all available scientific evidence for the effect of oral Se supplementation on milk Se concentration in cattle. The literature search was based on electronic and nonelectronic databases. Fixed- and random-effects models of meta-analysis were used, and a meta-regression was carried out to evaluate heterogeneity among studies. Random-effects meta-analysis was performed on 42 studies published between 1977 and 2007. Oral Se supplementation resulted in an average increase in milk Se content of 0.16 (95% confidence interval: 0.117, 0.207) micromol/L, with a significant heterogeneity among studies. Weak publication bias was evident, but it did not change the average effect. The continent where the study was performed, Se source, Se dose, and the interaction between source and dose explained 71% of the between-study variance. On average, American cows supplemented with Se yeast (e.g., 6 mg/h per day) had greater milk Se concentrations (approximately 0.37 micromol/L) 75 d after the beginning of supplementation when compared with those supplemented with inorganic forms of Se. This information provides a basis for tailoring daily animal requirements and for enhancing the Se intake of consumers of dairy products.
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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 | 0.015 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.042 |
| Bibliometrics | 0.004 | 0.004 |
| 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) | 0.002 | 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".