A Systematic Review of the Influence of Bovine Colostrum Supplementation on Leaky Gut Syndrome in Athletes: Diagnostic Biomarkers and Future Directions
Bibliographic record
Abstract
BACKGROUND: Bovine colostrum (BC) contains a myriad of bioactive molecules that are renowned for possessing unique medicinal benefits in children and adults, and BC supplements are considered safe and cost-effective options to manage/prevent the incidence of upper respiratory tract infections and gut-related problems in athletes. In this review, we will try to answer the question: How will BC supplementation ameliorate gut permeability problems among athletes? METHODS: Literature searches were performed using PRISMA guidance to identify studies assessing the influence of BC supplements on gut permeability. Studies were selected using four databases: PubMed, Web of Science, Scopus, and EBSCO, and a total number of 60 articles were retrieved by using appropriate keywords. RESULTS: Nine studies were selected that met the eligibility criteria for this review. The data analysis revealed that vigorous exercise profoundly increases intestinal permeability, and BC supplementation helps to reverse gut permeability in athletes. CONCLUSION: BC supplementation may be highly beneficial in improving gut permeability in athletes. However, well-designed, placebo-controlled, and randomized studies are needed to evaluate the long-term safety and efficacy and to determine the optimal dose schedules of BC supplementation in high-performance athletes.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".