Antimicrobial Resistance in Bovine Respiratory Disease Pathogens: A Systematic Review and Analysis of the Published Literature
Bibliographic record
Abstract
Bovine respiratory disease (BRD) is the most significant disease challenge in the feeder cattle industry in North America. Antimicrobials are commonly administered in BRD cases due to the role of bacterial pathogens. However, reports of antimicrobial resistance (AMR) in these pathogens raise concerns regarding their long-term effectiveness to treat BRD cases. A systematic literature review was conducted to summarize AMR in North American BRD pathogens and determine if changes in AMR prevalence over time could be identified for antimicrobials routinely used for treatment and control of BRD. Generalized linear models were used to test associations between the proportion of resistant isolates for each of the three bacterial agents and antimicrobial agent, isolation year, and timing of sample collection. The antimicrobial agent and timing of sample collection were significantly associated with the proportion of antimicrobial resistant isolates, with increased probability of resistance to tulathromycin seen in Mannheimia haemolytica (24.08%) and Histophilus somni (8.19%) and increased resistance to tildipirosin in Pasteurella multocida (21.48%), while samples collected at arrival demonstrated a lower proportions of resistant bacteria. Trends over time could not be evaluated due to the limited number of published studies. These findings highlight the differences in AMR seen between antimicrobials, BRD pathogens, and sample types and emphasize the need for continued AMR surveillance.
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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.015 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".