Características ultrassonográficas do hemangiossarcoma esplênico em cães: Revisão
Bibliographic record
Abstract
Ultrasonography is a diagnostic imaging method widely used in veterinary medicine, thus allowing for the evaluation of various organs in real time. Abdominal ultrasonography is fundamental to evaluate abdominal organs, such as the spleen, which has important functions in the body of animals and is predisposed to various pathological processes, including neoplastic ones. The most common splenic neoplasm is hemangiosarcoma, which is a tumor originating from endothelial cells of blood vessels, highly malignant and metastatic, which occurs more frequently in dogs with an average age of 9 to 12 years, and the most predisposed breeds are German Shepherds, Golden Retrievers, Labrador Retrievers and Schnauzers. The etiology of hemangiosarcoma in dogs remains uncertain and clinical signs are generally nonspecific, varying according to the site of origin of the primary tumor, the presence or absence of metastases and spontaneous rupture of the tumor. The diagnosis of this condition is made through the patient’s history and clinical signs, physical examination and complementary tests, thus allowing for an accurate assessment of the stage of the patient's disease. Abdominal ultrasound is important to detect splenic hemangiosarcoma, which is identified by its complex and heterogeneous echogenicity, consisting of anechogenic, hypoechogenic and hyperechogenic areas, caused by hemorrhage, necrosis and fibrotic or calcified tissue, and is often accompanied by peritoneal fluid. In addition, the ultrasound assists in directing the treatment and consequently improves the patient's quality of life. However, the definitive diagnosis can only be obtained through histopathological analysis of samples collected by ultrasound-guided biopsy or after splenectomy. Usually, the prognosis for hemangiosarcoma is poor. The objective of this work is to describe the sonographic features found in splenic hemangiosarcomas in dogs.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".