Informing Stewardship Measures in Canadian Food Animal Species through Integrated Reporting of Antimicrobial Use and Antimicrobial Resistance Surveillance Data—Part II, Application
Bibliographic record
Abstract
Using the methodology developed for integrated analysis and reporting of antimicrobial use (AMU) and antimicrobial resistance (AMR) data, farm-level surveillance data were synthesized and integrated to assess trends and explore potential AMU and AMR associations. Data from broiler chicken flocks (n = 656), grower–finisher pig herds (n = 462) and turkey flocks (n = 339) surveyed by the Canadian Integrated Program for Antimicrobial Resistance Surveillance (CIPARS) at the farm-level (2015–2019) were used. The analyses showed a reduction in mean flock/herd level number of defined daily doses using Canadian standards (nDDDvetCA) adjusted for kg animal biomass that coincided with the decline in % resistance in the three species. This was noted in most AMU-AMR pairs studied except for ciprofloxacin resistant Campylobacter where resistance continued to be detected (moderate to high levels) despite limited fluoroquinolone use. Noteworthy was the significantly negative association between the nDDDvetCA/kg animal biomass and susceptible Escherichia coli (multispecies data), an early indication that AMU stewardship actions are having an impact. However, an increase in the reporting of diseases in recent years was observed. This study highlighted the value of collecting high-resolution AMU surveillance data with animal health context at the farm-level to understand AMR trends, enable data integration and measure the impact of AMU stewardship actions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".