Enteropathogenic Bacteria in Dogs and Cats: Diagnosis, Epidemiology, Treatment, and Control
Bibliographic record
Abstract
This report offers a consensus opinion on the diagnosis, epidemiology, treatment, and control of the primary enteropathogenic bacteria in dogs and cats, with an emphasis on Clostridium difficile, Clostridium perfringens, Campylobacter spp., Salmonella spp., and Escherichia coli associated with granulomatous colitis in Boxers. Veterinarians are challenged when attempting to diagnose animals with suspected bacterial-associated diarrhea because well-scrutinized practice guidelines that provide objective recommendations for implementing fecal testing are lacking. This problem is compounded by similar isolation rates for putative bacterial enteropathogens in animals with and without diarrhea, and by the lack of consensus among veterinary diagnostic laboratories as to which diagnostic assays should be utilized. Most bacterial enteropathogens are associated with self-limiting diarrhea, and injudicious administration of antimicrobials could be more harmful than beneficial. Salmonella and Campylobacter are well-documented zoonoses, but antimicrobial administration is not routinely advocated in uncomplicated cases and supportive therapy is recommended. Basic practices of isolation, use of appropriate protective equipment, and proper cleaning and disinfection are the mainstays of control. Handwashing with soap and water is preferred over use of alcohol-based hand sanitizers because spores of C. difficile and C. perfringens are alcohol-resistant, but susceptible to bleach (1:10 to 1:20 dilution of regular household bleach) and accelerated hydrogen peroxide. The implementation of practice guidelines in combination with the integration of validated molecular-based testing and conventional testing is pivotal if we are to optimize the identification and management of enteropathogenic bacteria in dogs and cats.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".