The incidence and trends of proteinuria, azotemia and hypertension in cats receiving toceranib phosphate
Bibliographic record
Abstract
Objectives This retrospective study aimed to determine the incidence and trends of proteinuria, elevations in serum creatinine and urea, and systolic blood pressure in cats undergoing treatment with toceranib. Methods In total, 32 cats treated with toceranib for malignancies were analyzed. Cats were included if urinalysis and urine protein:creatinine ratio (UPC) measurements were available at 28 days (T1) and 56 days (T2) after starting the treatment. Cats with concurrent lower urinary tract disease, including urinary tract malignancy, were excluded. Friedman’s ANOVA compared variables between time points, and the Spearman test assessed the correlation between treatment duration and UPC. Results The median starting dose of toceranib was 2.68 mg/kg (range 1.7–3.9). In total, 15 (46.9%) cats received concurrent non-steroidal anti-inflammatory drugs. The most commonly treated tumors were oral squamous cell carcinoma (n = 10) and mast cell tumor (n = 5). None of the 32 cats developed progressive proteinuria or azotemia during the follow-up period (median 56 days; range 56–336). Notably, UPC and serum creatinine were significantly lower at T2 compared with baseline ( P = 0.012 and 0.001, respectively). Among the four cats with baseline proteinuria, UPC decreased over time with or without concurrent telmisartan treatment (n = 2). All four of these cats experienced a reduction in tumor size with toceranib concurrently with their decreased UPC. There was no significant correlation between UPC and the duration of toceranib treatment ( P = 0.089). Blood pressure was not significantly different over the assessed time points. Conclusions and relevance The incidence of proteinuria, renal azotemia and hypertension in cats treated with toceranib for neoplasia appears to be low. Toceranib may be a viable treatment option even in cats with pre-existing proteinuria or renal disease, with careful monitoring of trends recommended.
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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 | 0.003 | 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".