Effects of probiotic supplementation on growth performance and feed intake of dairy calves: A meta-analysis
Bibliographic record
Abstract
The objective of this systematic review and meta-analysis was to evaluate the effect of probiotic supplementation on ADG, feed intake, and feed efficiency (FE) of dairy calves. A secondary objective was to assess outcomes stratified by probiotic type. Our study included quasirandomized and randomized controlled trials written in English, Spanish, or Portuguese that assessed the effects of probiotic supplementation on the growth of dairy calves. No restrictions were placed on the publication year. A total of 5,480 records were initially identified after conducting searches in Biosis, CAB Abstracts, Medline, Scopus, and the Dissertations and Theses Database. After applying inclusion criteria, 55 studies (56 trials) were included in the analysis. Multilevel random-effects models were fitted for a single dataset combining all trials regardless of probiotic type and for 4 datasets stratified by 4 probiotic types (Bacillus, Lactobacillus, Saccharomyces, and multiple genera probiotics). Meta-analyses showed that probiotic supplementation did not result in significant difference in FE compared with the control group (no treatment or placebo). Probiotic supplementation improved total DMI, starter intake, and ADG and tended to decrease milk intake. A meta-regression analysis indicated a significant association between starter intake and probiotic type and the duration of probiotic supplementation. Analyses by probiotic type revealed no significant effects on DMI or FE for Lactobacillus spp., Saccharomyces spp., or multiple genera probiotics, whereas Bacillus spp. showed no effect on DMI but a tendency to improve FE. Supplementation with Lactobacillus spp. and multiple genera probiotics tended to increase starter intake. Supplementation with Bacillus spp. and Lactobacillus spp. increased the ADG of calves, whereas Saccharomyces spp. and multiple genera probiotic supplementation did not yield significant differences. Substantial and significant heterogeneity was observed for most outcomes; thus, results must be interpreted carefully. Probiotics may be beneficial for enhancing DMI, starter intake, and ADG in dairy calves; however, current evidence remains limited due to high heterogeneity. Results of analyses by probiotic type should be interpreted carefully due to the limited number of studies per category. To develop appropriate recommendations, additional research is required to address the sources of heterogeneity in existing studies.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.045 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| 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".