Neonatal Calf Diarrhea and Gastrointestinal Microbiota: Etiologic Agents and Microbiota Manipulation for Treatment and Prevention of Diarrhea
Bibliographic record
Abstract
Neonatal calf diarrhea is the leading cause of neonatal morbidity and mortality globally. The changes associated with the gastrointestinal microbiota in neonatal calves experiencing diarrhea and its etiology are not fully understood or completely defined in the literature. Several studies have demonstrated that the fecal microbiota of calves that experience diarrhea substantially deviates from that of healthy age-matched calves. However, one key question remains: whether the changes observed in the bacterial communities (also known as dysbiosis) are a predisposing factor for, or the consequence of, gastrointestinal inflammation caused by the pathogens associated with calf diarrhea. The first objective of this literature review is to present the current information regarding the changes in the fecal microbiota of diarrheic calves and the impact of the pathogens associated with diarrhea on fecal microbiota. Modulation of the gastrointestinal microbiota using pre- and probiotics, colostrum feeding, and fecal microbiota transplantation (FMT) has been used to treat and prevent gastrointestinal diseases in humans and dogs. Although information regarding the use of probiotics for the prevention of diarrhea is available in cattle, little information is available regarding the use of these strategies for treating calf diarrhea and the use of prebiotics or FMT to prevent diarrhea. The second objective of this literature review is to summarize the current knowledge regarding the impact of prebiotics, probiotics, synbiotics, colostrum feeding, and FMT for the treatment and prevention of calf diarrhea.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".