Occurrence of different neoplasms of dogs in Mumbai region
Bibliographic record
Abstract
The present study was conducted on 55 dogs presented with the history of tumorous growths to the department. Age, sex, breed and site of tumour/growth of all the animals were recorded. The biopsy samples were collected for histopathological examination. The age of affected animals varied from 1–15 years. The highest occurrence was recorded in the age group of 4–6 years (30.9%). Both male and female animals were equally affected. Skin neoplasms were found more in males (69.2%) than in females (30.8%). Three male animals (75%) were affected with oral tumours as compared to one case of female (25%). All cases of perianal adenoma/adenocarcinoma were seen in male animals only. Thirty one cases were observed in non-descript animals (50.9%) followed by Pomeranian and Labrador (n= 8, 14.5%), German Shepherd (n=5, 9.09%), Rottweiler (n=2, 3.63%) and Basset hound, Doberman, German short hair, Great Dane (n=1, 1.8%) respectively. Out of 55 suspected cases, eleven samples (20.01%) were diagnosed as inflammatory hyperplastic growths and forty four cases (80.0%) were diagnosed as neoplastic upon histopathology. Out of 44 cases, 14 cases were diagnosed as mammary tumours (benign 14.5% and malignant 10.9%),) followed by histiocytoma (8.6%), venereal granuloma (6.8%), perianal gland adenocarcinoma (3.6%), epulis (3.4%), fibroma (3.4%), leiomyosarcoma (3.4%), perianal gland adenoma (1.7%) leiomyoma (1.7%), seminoma (1.7%), sertoli cell tumour (1.7%), melanoma (1.7%), sqmaous cell carcinorma (1.7%), fibrosarcoma (1.7%), meibomian gland adenoma (1.7%), skull bone tumours (1.7%) and hemangiopericytoma (1.7%).
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".