Canine testicular tumors: An 11-year retrospective study of 358 cases in Moscow Region, Russia
Bibliographic record
Abstract
Background and Aim: Canine testicular tumors are among the most common reproductive tract tumors in male dogs and have been studied in many countries. However, to the best of our knowledge, studies with a large sample size have not been conducted in Russia. This study aimed to provide the latest information on the prevalence of canine testicular tumors in the Veterinary Oncology Scientific Center for Small Animals "Biocontrol" in Moscow, Russia, in 2010-2020 and the characteristics of the affected canine population. Materials and Methods: A retrospective review of patients and histological reports was collected and analyzed from 358 dogs with 447 testicular tumors within 11 years. Results: The mean age of the affected dogs was 10.4 years, whereas that of dogs with Sertoli cell tumors was 9.4 years p=0.009. This study includes mixed-breed dogs (18.4%), Yorkshire Terriers (8.8%), Labrador Retrievers (7.9%), Golden Retrievers (5.0%), and Fox Terriers (3.4%). The most common tumors were interstitial cell tumors (n=227, 50.8%). In contrast, 107 (23.9%) seminomas, 80 (17.9%) Sertoli cell tumors, 19 (7.4%) mixed germ cell-sex cord-stromal tumors, and 26 (7.6%) testicular tumors developed from cryptorchid testes, which included 16 (61.5%) Sertoli cell tumors, 10 (38.5%) seminomas, and no interstitial cell tumors. Conclusion: This study provides baseline information on the prevalence of canine testicular tumors in the described population, including the median age of each tumor type and overrepresented dog breeds. We further found that the most common scrotal testicular tumor was interstitial cell tumor, whereas Sertoli cell tumor was the most common in cryptorchid testicles.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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".