Toceranib phosphate in the treatment of canine thyroid carcinoma: 42 cases (2009‐2018)
Bibliographic record
Abstract
Thyroid carcinoma is the most common endocrine malignancy in dogs. Thyroidectomy and radiation therapy control local disease, yet are not always feasible, and efficacious medical therapies need to be identified. Toceranib phosphate has been reported to provide clinical benefit (CB) in dogs with thyroid carcinoma, while its role in treatment-naïve thyroid tumours has not been well-described. The objective of this study was to describe the use of toceranib in the management of thyroid carcinomas in dogs in both the naïve-disease and prior therapy- settings. A medical record search identified 42 dogs diagnosed with thyroid carcinoma and treated with toceranib, of which 26 and 16 dogs were in settings of naïve-disease and after prior therapy, respectively. Twenty-three (88.4%) and twelve (75%) dogs experienced CB in the naïve and prior therapy settings, respectively. The median [95% confidence interval] progression free interval (PFI) for dogs in the naïve and prior therapy settings were 206 [106,740] and 1015 [92,1015] days, respectively. The median overall survival time (OST) for dogs in the naïve and prior therapy settings were 563 [246,916] and 1082 [289,1894] days, respectively. Overall, the data provided no evidence for differences in overall PFI (P > .20) or OST (P = .15) between settings. However, when asymptomatic at the time of diagnosis, dogs in the naïve setting showed poorer survival prognosis (estimated hazard ratio 17.2 [1.8, 163]) relative to dogs in the prior therapy setting. This study characterizes PFI, OST and CB with minimal AE in dogs with thyroid carcinoma treated with toceranib in both the naïve and prior therapy settings.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".