The Influence of Dog Body Conditions on the Risk of Mastopathy
Bibliographic record
Abstract
The article is devoted to the study of mastopathy risk assessment in overweight dogs. According to histopathological verification, among malignant neoplasms, ductal carcinoma is the most common (25%, 95% CI: 22–28%), benign tumors are fibroadenoma (20.6, 95% CI: 19–23%), dysplasia / hyperplasia is ductal ectasia (22.8%, 95% CI: 19–27%). According to the results of the statistical analysis, 30.7% (95% CI: 25–36%) of overweight bitches suffered from mastopathy, 46.5% (95% CI: 40–54%) from benign neoplasia, and 52.7% (95% CI: 47–59%) from malignant mammary neoplasms. It was established that over the past 3 years (from 2020 to 2022), the incidence of fibrocystic disease in bitches with excess body weight increased from 18.8% (95% CI: 16–22%) to 41.0% (95% CI: 38–44%) against the background of a decrease among patients with optimal body weight from 72.3% (95% CI: 69–76%) to 49.7% (95% CI: 45–55%). The risk of mastopathy correlates with the degree of overweight: with the exceeding of the optimal condition within 20%, the incidence was 20.8% (95% CI: 17–24%), for 30–50% it was 30.8% (95% CI: 23–38%), and for more than 50% it was 48.4% (95% CI: 41–56%). Compared to bitches with malignant mammary tumors, average body mass indices in dogs with benign neoplasia and mastopathy are significantly higher (p < .05 and p < .001, respectively). The number of patients with mastopathy increases with age; the maximum indicators were set in 91-year-old (35.8%, 95% CI: 29–42%) and older 11-year-old (32.6%, 95% CI: 26–39%) animals. German shepherd, Labrador retriever, boxer, poodle, and dachshund females are the most susceptible to fibrocystic disease (12.5%, 10.0%, 9.2%, 8.3%, and 8.3%, respectively), as well as mixed breeds (13.4%). The dynamic increase in the number of overweight bitches suffering from mastopathy confirms the importance of obesity in its pathogenesis. Cite this article as: Bilyi, D., & Khomutenko, V. (2024). The in!uence of dog body conditions on the risk of mastopathy. Acta Veterinaria Eurasia, 50(1), 37-46
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".