Epidemiologia do mastocitoma em cães em uma região do Mato Grosso do Sul
Bibliographic record
Abstract
Cutaneous mastocytoma is a neoplasm often seen in dogs. This disease is characterized by abnormal and excessive growth of mast cells. This study carried out the data collection of all biopsy files made in dogs in the clinical pathology laboratory of the University Center of Grande Dourados and Universidade Anhaguera Uniderp between January 2015 and January 2016. Twenty-six confirmed cases of mastocytoma From the information collected from the reports, predisposing factors such as race, sex, age and site of neoplastic lesions were evaluated. Among the selected animals, 46.2% (12) were females and 53.8% (14) males. Regarding the racial factor, 34.6% (9) of the animals had no defined race, 30.8% (8) were of the Boxer breed, 11.5% (3) were Pitbull breed, 7.7% ) Of the Labrador breed, 7.7% (2) were of the Dachshund breed, 3.8% (1) was Poodle breed and 3.8% (1) of the Shnauzer breed. It was concluded in this study that the majority of the dogs affected by this neoplasia were undefined. When considered the breed it was observed that Boxer dogs are the most predisposed to this neoplasia. Animals considered elderly, aged between 9 and 13 years are the most affected. The canine mastocytoma showed no predisposition to the disease as to sex, and it was possible to verify the higher frequency in relation to the location of the nodules in the part of the trunk and limbs.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 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".