Efficacy of antimicrobial and nutraceutical treatment for canine acute diarrhoea: A systematic review and meta-analysis for European Network for Optimization of Antimicrobial Therapy (ENOVAT) guidelines
Bibliographic record
Abstract
Systemic antimicrobial treatments are commonly prescribed to dogs with acute diarrhoea, while nutraceuticals (prebiotics, probiotics, and synbiotics) are frequently administered as an alternative treatment. The aim of this systematic review and meta-analysis was to assess the effectiveness of antimicrobials and nutraceutical preparations for treatment of canine acute diarrhoea (CAD). The results of this study will be used to create evidence-based treatment guidelines. PICOs (population, intervention, comparator, and outcome) were generated by a multidisciplinary expert panel taking into account opinions from stakeholders (general practitioners and dog owners). The Grading of Recommendations Assessment, Development and Evaluation (GRADE) methodology was used to evaluate the certainty of the evidence. The systematic search yielded six randomised controlled trials (RCT) for antimicrobial treatment and six RCTs for nutraceutical treatment meeting the eligibility criteria. Categories of disease severity (mild, moderate, and severe) were created based on the presence of systemic signs and response to fluid therapy. Outcomes included duration of diarrhoea, duration of hospitalization, progression of disease, mortality, and adverse effects. High certainty evidence showed that antimicrobial treatment did not have a clinically relevant effect on any outcome in dogs with mild or moderate disease. Certainty of evidence was low for dogs with severe disease. Nutraceutical products did not show a clinically significant effect in shortening the duration of diarrhoea (based on very low to moderate certainty evidence). No adverse effects were reported in any of the 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 | 0.029 | 0.059 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.050 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".