Histopathological classification and incidence of canine mammary tumours
Bibliographic record
Abstract
The present study aimed to classify different canine mammary tumours based on WHO recommendations. A total of 139 suspected spontaneous tumours were collected, out of which 128 were diagnosed as tumours. The benign tumours were identified as fibroadenoma (41.66%), ductal papilloma (16.66%), benign mixed mammary tumour (29.16%), myoepithelioma (4.16%) and simple adenoma (8.33%). In malignant mammary tumours, epithelial tumours included papillary adenocarcinoma (25.96%), malignant mixed mammary tumour (25.96%), solid carcinomas (17.31%), infiltrative adenocarcinoma (11.54%), malignant myoepithelioma (7.69%), squamous cell carcinoma (2.88%), mucinous carcinoma (1.92%), intraductal carcinoma in situ (0.96%), whereas the connective tissues tumours were fibrosarcoma (2.88%), myxosarcoma (0.96%), carcinosarcoma (0.96%) and osteochondrosarcoma (0.96%). Analysis of breed-wise occurence of mammary neoplasms revealed highest number of tumours in German shepherd (35.0%) followed by Spitz (24.22%), non-descript (19.53%), Pomeranian (10.94%), Labrador (6.25%), Boxer (3.91%), Doberman (4.69%), Cocker Spaniel (3.13%), Bhutia (1.56%) and Great Dane (0.78%). The age group at which mammary tumours occurred most frequently was 8–10 years (46), followed by 6–8 years (39), 10–12 years (23), ≤ 6 years (16) and >12 years (4).
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 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 | 0.001 | 0.000 |
| 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 | 0.001 |
| 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".