Effect of antimicrobial administration on fecal microbiota of critically ill dogs: dynamics of antimicrobial resistance over time
Bibliographic record
Abstract
BACKGROUND: Multidrug resistance in companion animals poses significant risks to animal and human health. Prolonged antimicrobial drug (AMD) treatment in animals is a potential source of selection pressure for antimicrobial resistance (AMR) including in the gastrointestinal microbiota. We performed a prospective study of dogs treated for septic peritonitis, pyometra, or bacterial pneumonia and collected repeated fecal samples over 60 days. Bacterial cultures and direct molecular analyses of fecal samples were performed including targeted resistance gene profiling. RESULTS: Resistant Escherichia coli increased after 1 week of treatment (D1:21.4% vs. D7:67.9% P < 0.001) and returned to baseline proportions by D60 (D7:67.9% vs D60:42.9%, P = 0.04). Dogs with septic peritonitis were hospitalized significantly longer than those with pneumonia or pyometra. Based on genetic analysis, Simpson's diversity index significantly decreased after 1 week of treatment (D1 to D7, P = 0.008), followed by a gradual increase to day 60 (D1 and D60, P = 0.4). Detection of CTX-M was associated with phenotypic resistance to third-generation cephalosporins in E. coli (OR 12.1, 3.3-68.0, P < 0.001). Lincosamide and macrolide-resistance genes were more frequently recovered on days 14 and 28 compared to day 1 (P = 0.002 and P = 0.004 respectively). CONCLUSION: AMR was associated with prescribed drugs but also developed against AMDs not administered during the study. Companion animals may be reservoirs of zoonotic multidrug resistant pathogens, suggesting that veterinary AMD stewardship and surveillance efforts should be prioritized.
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".