Laboratory Testing to Inform Antimicrobial Use for Bovine Respiratory Disease: Perceptions of Canadian Feedlot Veterinarians
Bibliographic record
Abstract
Timely, accurate, and reliable laboratory tools are important for antimicrobial stewardship (AMS) in food animal production. The objectives of this study were to (1) identify factors that influence live animal respiratory sample collection for laboratory testing and (2) describe the potential for integration of laboratory testing of samples from live animals into an AMS strategy for bovine respiratory disease (BRD) management in Canadian feedlots. Utilizing focused ethnography, virtual key informant interviews were conducted with eight feedlot veterinarians from Alberta, Ontario, and Saskatchewan, Canada. Thematic analysis revealed four themes: (1) lived experience of feedlot veterinarians with laboratory testing for BRD pathogens and antimicrobial resistance (AMR), (2) evidence-informed BRD management that integrates multiple data sources and their components, (3) organizational factors that affect the uptake and use of laboratory tests, and (4) the role of laboratory testing to support AMS in BRD management. While sample collection and laboratory testing are commonly used for BRD and AMR research and surveillance, they are not routinely applied in everyday BRD management. Veterinarians identified key challenges with laboratory testing, including turnaround time, the need for clear benefits, practical implementation, and effective communication with feedlot managers. Laboratory testing must provide valuable, actionable insights to encourage its adoption in Canadian feedlot operations. If successful, such testing could support AMS and be used to justify antimicrobial use if this were to become a regulatory or market requirement.
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 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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".