Antimicrobial use in swine production and its effect on the swine gut microbiota and antimicrobial resistance
Bibliographic record
Abstract
Antimicrobials have been used in swine production at subtherapeutic levels since the early 1950s to increase feed efficiency and promote growth. In North America, a number of antimicrobials are available for use in swine. However, the continuous administration of subtherapeutic, low concentrations of antimicrobials to pigs also provides selective pressure for antimicrobial-resistant bacteria and resistance determinants. For this reason, subtherapeutic antimicrobial use in livestock remains a source of controversy and concern. The swine gut microbiota demonstrates a number of changes in response to antimicrobial administration depending on the dosage, duration of treatment, age of the pigs, and gut location that is sampled. Both culture-independent and -dependent studies have also shown that the swine gut microbiota contains a large number of antimicrobial resistance determinants even in the absence of antimicrobial exposure. Heavy metals, such as zinc and copper, which are often added at relatively high doses to swine feed, may also play a role in maintaining antimicrobial resistance and in the stability of the swine gut microbiota. This review focuses on the use of antimicrobials in swine production, with an emphasis on the North American regulatory context, and their effect on the swine gut microbiota and on antimicrobial resistance determinants in the gut microbiota.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".