Comparison of treatment records and inventory of empty drug containers to quantify antimicrobial usage in dairy herds
Bibliographic record
Abstract
Assessment of antimicrobial use (AMU) is vital for interpreting the origin of changes in antimicrobial resistance (AMR). The objectives of the present study were to estimate the association between AMU determined using on-farm treatment records (TR) and inventory of empty drug containers (INV). Herds were selected to represent Canadian dairy farms. Producers were asked to record animal health events and treatments on a standard General Health Event form. For inventory data, 40-L receptacles were placed at various locations considered convenient to deposit all empty drug containers. Antimicrobial defined-daily dosages (ADD) were calculated for 51 Canadian herds using the 2 methods. Estimation of AMU was 31,840 ADD using the INV and 14,487 ADD using the TR, indicating that for every TR entry, 2.20 times more treatments were observed using the INV. Mastitis, reproductive conditions, and dry cow therapy were the most frequent reasons for antimicrobial therapy when assessing TR. For all antimicrobials evaluated, mean ADD was higher using the INV versus TR. Regardless, a strong positive correlation (0.80) was observed between the 2 methods, indicating that herds with increased number of ADD recorded using the INV also had increased number of ADD recorded using TR. Furthermore, a positive association was observed for the 6 most commonly used antimicrobials. In comparison to methods used in surveillance programs on AMU in livestock that assume a constant use in all herds (i.e., sales data), INV provided a herd-level specific quantity of AMU positively correlated with AMU recorded at the animal level in general. The INV was easy to implement and provided a measure of total AMU in the herd. Availability of such information would be valuable for interpreting changes in AMR at the herd level and enabling evaluation of interventions for decreasing AMR.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| 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".