Clínica médica e cirúrgica de animais de companhia
Bibliographic record
Abstract
O relatório de estágio foi elaborado no âmbito da finalização do mestrado integrado em Medicina Veterinária. O presente trabalho é dividido em duas partes distintas. A primeira componente consiste na apresentação da casuística acompanhada no estágio. A afeção oncológica mais comum foi o linfoma canino. A segunda componente compreende uma revisão bibliográfica do osteossarcoma apendicular e axial canino complementada com um caso clínico acompanhado no estágio. O osteossarcoma canino é a neoplasia óssea mais comum em indivíduos de meia-idade a idosos, sobretudo de raças grandes e gigantes. O diagnóstico compreende uma abordagem multifatorial que envolve exames histopatológicos, imagiológicos e serológicos. O tratamento recomendado é a combinação de cirurgia, para remoção do tumor primário, com protocolos de quimioterapia, para controlo das metástases. Novas terapias estão a emergir de forma a tornar os tratamentos mais eficazes; Abstract: Small animal clinic and surgery This internship report was written as part of the conclusion of the integrated master’s degree in veterinary medicine. This work was divided into two distinct parts. The first one covers the casuistry followed during the internship. Canine lymphoma was the most frequent oncologic diseases. The second component consists of literature review of appendicular and axial canine osteosarcoma, along with the report of a case followed during the internship. Canine osteosarcoma is the most common bone neoplasm in middle-aged and elderly individuals, especially in large and giant breeds. The diagnosis involves a multifactorial approach of histopathology and advanced imaging and blood work. The recommended treatment is the combination of surgery, for primary tumor resection, and chemotherapy protocols to metastases control. In order to achieve more effective treatments, new therapies are being researched.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".