Association of histological features with potential risk factors and survival in canine mammary tumors
Bibliographic record
Abstract
The epidemiological and clinicopathological features of canine mammary tumors may provide valuable information to facilitate analysis of the behavior of the disease and represent a potential tool for the study of breast cancer in women. The aim of this study was to associate the histological features of canine mammary tumors with potential risk factors and survival. One hundred and seventy-eight mammary tumors were collected from 80 female dogs. The statistical analyses consisted of a series of univariate studies and frequencies of the different study variables, such as a bivariate analysis with the Chi squared test (χ2), a relative risk and Kaplan Meier survival analysis, and a multiple correspondence analysis was used to correlate the tumor’s biological behavior with the dogs’ breed. Most patients were older than 8 years and had at least one malignant tumor, which was usually solitary and measured more than 6 cm; these patients had poor survival. The most frequent tumors were a complex adenoma, benign mixed tumor, carcinoma complex and mixed type carcinoma. The most commonly affected breeds were Poodle, Cocker Spaniel and Dachshund, and the breeds at the highest risk of tumor development were Cocker Spaniel, Labrador Retriever and German Shepherd. Overall, the data indicated that mammary tumors in dogs mainly affected older females with malignant tumors and that there were high mortality and short-term survival rates. However, the most commonly affected breeds were not necessarily the most susceptible. Our data do not support the hypothesis of an increased risk of canine mammary tumors in nulliparous female dogs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".