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 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".