Non-antibiotic approaches for disease prevention and control in beef and veal production: a scoping review
Bibliographic record
Abstract
Livestock producers are encouraged to reduce the use of antibiotics belonging to classes of medical importance to humans. We conducted a scoping review on non-antibiotic interventions in the form of products or management practices that could potentially reduce the need for antibiotics in beef and veal animals living under intensive production conditions. Our objectives were to systematically describe the research on this broad topic, identify specific topics that could feasibly support systematic reviews, and identify knowledge gaps. Multiple databases were searched. Two reviewers independently screened and charted the data. From the 13,598 articles screened, 722 relevant articles were charted. The number of relevant articles increased steadily from 1990. The Western European research was dominated by veal production studies whereas the North American research was dominated by beef production studies. The interventions and outcomes measured were diverse. The four most frequent interventions included non-antibiotic feed additives, vaccinations, breed type, and feed type. The four most frequent outcomes were indices of immunity, non-specific morbidity, respiratory disease, and mortality. There were seven topic areas evaluated in clinical trials that may share enough commonality to support systemic reviews. There was a dearth of studies in which interventions were compared to antibiotic comparison groups.
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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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".