Antimicrobial Resistance in Fecal <i>Escherichia coli</i> and <i>Salmonella enterica</i> from Dairy Calves: A Systematic Review
Bibliographic record
Abstract
The discovery of antibiotics brought with it many advances in the health and well-being of humans and animals; however, in recent years development of antimicrobial resistance (AMR) has increasingly become a concern. Much of the antibiotic use on dairy farms is for disease management in mature cattle, and AMR in fecal organisms is relatively rare in this group. However, young dairy calves often carry high levels of AMR in their fecal Escherichia coli and Salmonella enterica, which could provide a potential reservoir of AMR genes on dairy farms. To develop practical and effective antibiotic stewardship policies for dairy calf rearing, it is vital to have a solid understanding of the current state of knowledge regarding AMR in these animals. A systematic review process was used to summarize the current scientific literature regarding AMR in fecal S. enterica and E. coli and associations between management practices and AMR prevalence in dairy calves in the United States and Canada. Seven online databases were searched for literature published from 1997 to 2018. Multiple studies indicated an association between preweaned calves and increased risk of fecal shedding of resistant bacteria, compared to other animal groups on dairy farms. There also was evidence, although less consistent, of an impact of antibiotic treatment, antibiotic-containing milk replacer feeding, and feeding nonsalable or waste milk (WM) on the presence of AMR bacteria. Overall, the research summarized in this systematic review highlights the need for continued research on the impact of management practices, including antibiotic use, WM feeding, and disease prevention practices in reducing AMR in E. coli and S. enterica in dairy calves. In addition, few data were available on physiological and microbiological factors that may contribute to the high relative populations of resistant bacteria in young calves, suggesting another valuable area of future research.
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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 | 0.006 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".