Treatment of proteinuria in dogs with telmisartan: A retrospective study
Bibliographic record
Abstract
BACKGROUND: Use of telmisartan for the treatment of proteinuria in dogs has not been thoroughly investigated. HYPOTHESIS/OBJECTIVES: Telmisartan can be effective for the treatment of proteinuria in dogs. ANIMALS: Forty-four client-owned dogs with proteinuria. METHODS: Retrospective study. Dogs diagnosed with clinically relevant proteinuria (nonazotemic dogs with a urine protein-to-creatinine ratio [UPC] ≥2 and azotemic dogs with UPC ≥0.5) were separated into 3 groups: telmisartan alone, with benazepril, or with mycophenolate. The UPC was recorded before treatment and at subsequent follow-ups (1, 3, 6, and 12 months, as available). Response to treatment was categorized as complete (UPC ˂0.5), partial (UPC decreased by ≥50% but still ≥0.5), or no response (UPC decreased by <50%). Serum creatinine and potassium concentrations and arterial pressure also were recorded. RESULTS: In the telmisartan group, treatment response (UPC ˂0.5 or decreased by ≥50%) was observed in 70%, 68%, 80%, and 60% of dogs at 1, 3, 6, and 12 months follow-up, respectively. No significant changes were noted in serum creatinine or potassium concentrations, or in arterial blood pressure at all follow-up times. Adverse effects consisted of mild self-limiting gastrointestinal signs in 5 dogs. Two dogs developed clinically relevant azotemia that required discontinuation of the treatment before the first follow-up. CONCLUSIONS AND CLINICAL IMPORTANCE: Telmisartan can be considered for treatment of proteinuria in dogs, alone or in combination with other treatments for proteinuria.
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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 | 0.001 | 0.000 |
| 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) | 0.001 | 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 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".