Risk assessment modelling of fecal shedding caused by extended-spectrum cephalosporin-resistant Escherichia coli transmitted through waste milk fed to dairy pre-weaned calves
Bibliographic record
Abstract
Waste milk feeding is a common practice in dairy operations. Regardless of the benefits of this practice to the dairy farmers, concerns from the potential dissemination of antimicrobial-resistant bacteria through the gut and subsequent shedding by calves into the environment are increasing. In this study, we employed Monte Carlo simulation to assess the risk of shedding extended-spectrum cephalosporin-resistant Escherichia coli (ESC-R E. coli ) caused by waste milk feeding in pre-weaned calves using an exponential dose-response model fit to data for E. coli O157:H7 in cattle. Data from pertinent studies were included in our model to predict the risk of shedding. The median (5th and 95th percentiles) for the daily risk of shedding ESC-R E. coli by calves fed only contaminated waste milk was predicted to be 2.9 × 10 −3 (2.1 × 10 −3 , 3.7 × 10 −3 ), representing a median daily risk of 29 out of 10,000 calves shedding ESC-R E. coli due to exclusive feeding of waste milk containing ESC-R E. coli . This median value was reduced by 94% when accounting for the proportion of waste milk that does not contain ESC-R E. coli . The overall risk of shedding ESC-R E. coli through the pre-weaning period for farms that feed waste milk to calves was 5.7 × 10 −3 (2.4 × 10 −3 , 1.1 × 10 −2 ), representing 57 out of 10,000 calves. When accounting for the proportion of farms that do not feed waste milk, the pre-weaning period risk was reduced by 23%. By varying the prevalence of ESC-R E. coli in waste milk using values of 3, 1.5, and 1%, the daily risk of shedding decreased by factors of 50, 65, and 82%, respectively, which supports the reduction of contamination or discontinuation of feeding waste milk containing ESC-R E. coli as major mitigation measures to reduce the risk of shedding caused by ingestion of resistant bacteria. It is anticipated that the effects of antimicrobial residues in waste milk, which was not considered herein due to lack of data, would further increase risks. Although waste milk feeding to calves may be economically beneficial to the dairy farmers, there exists the risk of dissemination of ESC-resistant bacteria into the environment.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".