Adoption and decision factors regarding selective treatment of clinical mastitis on Canadian dairy farms
Bibliographic record
Abstract
As clinical mastitis (CM) treatments are responsible for a large portion of antimicrobial use on dairy farms, many selective CM treatment protocols have been developed and evaluated against a blanket treatment approach of CM cases. Selective treatment protocols use outcomes of diagnostic tests to exclude CM cases from antimicrobial treatment when they are unlikely to benefit. To tailor interventions to increase uptake of selective treatment strategies, a comprehension of current on-farm treatment practices and factors affecting treatment decisions is vital. Two questionnaires were conducted among 142 farms across 5 provinces participating in the Canadian Dairy Network for Antimicrobial Stewardship and Resistance in this cross-sectional study. Self-reported adoption of selective CM treatments by dairy farmers was 64%, with median of 82% of cows treated in those herds using selective treatment. Using logistic regression models, the odds to implement a selective CM treatment protocol increased with a decreasing average cow somatic cell count. No other associations were identified between use of a selective CM treatment protocol and farm characteristics (herd size, CM incidence, province, milking system, and housing system). Three subsets of farmers making cow-level CM treatment decisions were identified using a cluster analysis approach: those who based decisions almost exclusively on severity of clinical signs, those who used various udder health indicators, and farmers who also incorporated more general cow information such as production, age, and genetics. When somatic cell count was considered, the median threshold used for treating was >300,000 cells/mL at the last Dairy Herd Improvement test. Various thresholds were present among those considering CM case history. Veterinary laboratories were most frequently used for bacteriological testing. Test results were used to start, change, and stop treatments. Regardless of protocol, reasons for antimicrobial treatment withheld included cow being on a cull list, having a chronic intramammary infection, or being at end of lactation (i.e., close to dry off). If clinical signs persisted after treatment, farmers indicated that they would ask veterinarians for advice, stop treatment, or continue with the same or different antibiotics. Results of this study can be used to design interventions targeting judicious mastitis-related antimicrobial use, and aid discussions between veterinarians and dairy producers regarding CM-related antimicrobial use.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".