Non-antibiotic Approaches for Disease Prevention and Control in Nursery Pigs: A Scoping Review
Bibliographic record
Abstract
Swine producers are encouraged to practice antibiotic stewardship by reducing their use of antibiotics belonging to classes of medical importance to humans. We conducted a scoping review of non-antibiotic approaches in the form of products or management practices that might prevent or control disease and thus reduce the need for antibiotics in nursery pigs. Our objectives were to systematically describe the research on this broad topic for the North American context, identify specific topics that could feasibly support systematic reviews, and identify knowledge gaps. A search of multiple databases identified 11,316 articles and proceedings for relevance screening. From these, 441 eligible clinical trials and observational studies were charted. The majority were clinical trials (94%). Study results from EU countries were mostly communicated through journal articles, whereas study results from the USA were mostly communicated through conference proceedings. Interventions and health outcomes were diverse. The two most frequent intervention categories were feed additives and piglet vaccines. The three most frequent outcomes reported were diarrhea, mortality, and indices of vaccine immunity. There were 13 specific topics comprising various feed additives and vaccines that might feasibly support systematic reviews. There were relatively few studies in which interventions were compared with antibiotic comparison groups and relatively few studies evaluating management practices.
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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 | 0.010 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".