Salmonella bacteriuria in a cat fed a Salmonella-contaminated diet
Bibliographic record
Abstract
CASE DESCRIPTION: A 9-year-old castrated male domestic shorthair cat was evaluated because of hematuria and weight loss after an 8-year history of intermittent signs of feline lower urinary tract disease (FLUTD). A complete diet history revealed that the cat was eating a commercial diet that does not undergo the same processing procedures as most pet foods and so might be at increased risk for bacterial contamination owing to a nonstandard industry cooking procedure. CLINICAL FINDINGS: The cat had a history consistent with FLUTD, but bacteriologic culture of the urine revealed Salmonella organisms. Additional analysis revealed Salmonella enterica serotype I:ROUGH-O:g,m,s:- in samples of urine and feces as well as Salmonella enterica serotype Johannesburg and Salmonella enterica serotype Senftenberg in the diet. TREATMENT AND OUTCOME: The cat responded positively to antimicrobial treatment for the Salmonella bacteriuria as well as to dietary and environmental management for the clinical signs associated with FLUTD. CLINICAL RELEVANCE: Findings in this case highlighted an additional health consequence associated with ingestion of Salmonella-contaminated food. Such contamination is of particular concern with raw meat-based diets or diets that have not undergone standard industry cooking practices. Veterinarians should obtain a diet history for every companion animal during every evaluation to help with diagnosis and optimal treatment.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".