Evaluation of three herbal compounds used for the management of lower urinary tract disease in healthy cats: a pilot study
Bibliographic record
Abstract
OBJECTIVES: Lower urinary tract disease (LUTD) occurs commonly in cats, and idiopathic cystitis (FIC) and urolithiasis account for >80% of cases in cats <10 years of age. Although several strategies have been recommended, a common recommendation is to induce dilute urine resulting in more frequent urination and to dilute calculogenic constituents. In addition to conventional therapy using modified diets, traditional Chinese and Western herbs have been recommended, although only one - choreito - has published data available. We evaluated three commonly used herbal treatments recommended for use in cats with LUTD: San Ren Tang, Wei Ling Tang and Alisma. We hypothesized that these three Chinese herbal preparations would induce increased urine volume, decreased urine saturation for calcium oxalate and struvite, and differences in mineral and electrolyte excretions in healthy cats. METHODS: Six healthy spayed female adult cats were evaluated in a placebo-controlled, randomized, crossover design study. Cats were randomized to one of four treatments, including placebo, San Ren Tang, Wei Ling Tang or Alisma. Treatment was for 2 weeks each with a 1 week washout period between treatments. At the end of each treatment period, a 24 h urine sample was collected using modified litter boxes. RESULTS: Body weights were not different between treatments. No differences were found in 24 h urinary analyte excretions, urine volume, urine pH or urinary saturation for calcium oxalate or struvite between treatments. CONCLUSIONS AND RELEVANCE: The results of this study do not support the hypothesis; however, evaluation of longer-term and different dosage studies in cats with LUTD is warranted.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".