Conventional and unsupervised artificial intelligence analyses identified risk factors for antimicrobial resistance on dairy farms in the province of Québec, Canada
Bibliographic record
Abstract
Antimicrobial resistance (AMR) is one of the greatest threats to global health worldwide and is threatening not only humans, but also animal production systems, including dairy farms. The objective of this paper was to describe risks factors associated with AMR on dairy farms in Québec, Canada. This observational cross-sectional study included 101 commercial dairy farms and took place over a one-year period between the spring of 2017 and the spring of 2018. We explored risk factors such as farm practices and producer knowledge (measured using a questionnaire), antimicrobial use (quantified using veterinary invoices), and the presence of Salmonella Dublin (tested by serology). We evaluated AMR with fecal Escherichia coli retrieved from preweaning calves and lactating cows using the following outcomes: the presence of extended-spectrum β-lactamase/AmpC resistance and the number of resistances to antimicrobial classes. We used logistic regression models to evaluate the association between each risk factor and the 2 outcomes for the 2 types of samples (preweaning calves and lactating cows). Furthermore, we explored the relationships between these risk factors utilizing data dimensionality reduction and hierarchical clustering. Outputs of these analyses were used as regressors for AMR in regression models. Although the results for univariate analyses were ambiguous, the unsupervised analysis naturally categorized the sample of farms according to their health and treatment status (dimension 1, explaining 12.9% of the variance) and herd size (dimension 2, explaining 7.8%). Three clusters of farms were identified (cluster 1: mainly healthy herds and low ceftiofur users, cluster 2: relatively high ceftiofur users, cluster 3: farms with a higher incidence of diseases and higher antimicrobial treatment rates). Dimension 1 and cluster membership were statistically associated with the presence of extended-spectrum β-lactamase/AmpC resistance in lactating cows and in preweaning calves. Dimension 1 was also statistically associated with the number of resistances in lactating cows and in preweaning calves. This study highlights the complexity of analyzing risk factors associated with AMR. Our results suggest that the herd health status and the antimicrobial use-related practices used are associated with AMR in dairy farms. However, prospective studies are needed to confirm a causal relation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".