Primary fibrosarcoma im small intestine of dog - case report.
Bibliographic record
Abstract
ABSTRACT. Magalhães G.M., Santilli J., Calazans S.G., Nishimura L.T., Cerejo S.A. & Dias F.G.G. [Primary fibrosarcoma im small intestine of dog - Case report.] Fibrossarcoma primário em intestino delgado de cão - Relato de caso. Revista Brasileira de Medicina Veterinária, 37(2):145-148, 2015. Programa de Pós-Graduação em Medicina Veterinária de Pequenos Animais, Universidade de Franca, Av. Dr. Armando Salles Oliveira, 201, Cx postal 82, Parque Universitá- rio, Franca, SP 14404-600, Brasil. E-mail: georgiamode@hotmail.com Intestinal neoplasms are uncommon in dogs, and among the most frequently diagnosed are smooth muscle, lymphomas and carcinomas. The fibrosarcoma is extremely rare in the intestine of animals of this species therefore, little is known about the macroscopic and behavior of this tumor. Given this unusual intestinal disease in dogs, the present study aimed to report a case of intestinal fibrosarcoma in a poodle breed dog, 15 years old, with no apparent clinical signs. The mass was pedunculated, whitish and firm consistency. The diagnosis was made by histopathology. After four months of surgical excision, there was no recurrence and metastasis. We conclude that the intestinal fibrosarcoma has low aggressiveness, are rare and can present macroscopic pediculated.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".