Incidence and Histopathological Studies on Tumours of Dog in Bengaluru, India
Bibliographic record
Abstract
The current research work was undertaken with the objective of evaluating incidence, and histological study of canine tumours. 68 samples suspected for neoplasia from cutaneous and mammary gland origin were collected from dogs over a period of six months and were classified according to WHO classification (2002). Tumours of mesenchymal origin showed an incidence of 76.47 per cent and incidence of epithelial origin at 23.53 per cent of dogs. The highest incidence of tumours was found in female dogs than in male dogs. Age wise incidence of tumours was found between the range of 2-15 years with a mean age of 8.3 years. The breed wise incidence of tumours was highest in Labrador retriever (22.06%), followed by non-descriptive breeds (19.12%), Golden retriever (17.65%), German Shepherd dog (11.8%), Pomeranian (7.35%), Lhasa Apso (4.41 %), Daschound (4.41%), Boxer (2.94%), Saint Bernard (2.94%), Doberman (2.94%), Mudhol hound (2.94%) and Rajapalyam (1.44%). Based on histopathological classification, the incidence of cutaneous tumours was 77.94 per cent and mammary gland tumours were 22.06 per cent. Among cutaneous tumours, Mast cell tumour (17.64%) occurrence was highest followed by Hemangiosarcoma and Hepatoid gland carcinoma at 8.82% each. The occurrence of mammary gland tumours were also recorded and classified as Tubular (8.82%), Papillary (4.41%), Papillary cystic (5.88%) and carcinosarcoma (2.94%).
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".