Estudo retrospectivo de cães submetidos a tratamento quimioterápico no Setor de Oncologia do Hospital de Clínicas Veterinárias da UFRGS
Bibliographic record
Abstract
Com o aumento da expectativa de vida e do avanço da medicina veterinária, o diagnóstico de neoplasias em animais de companhia vem sendo cada vez mais comum. Estudos epidemiológicos e levantamentos de dados clínicos são importantes para que se possa determinar a prevalência de tumores e fatores de riscos nas populações de animais. Neste estudo retrospectivo foi realizado um levantamento de dados dos cães que realizaram tratamento quimioterápico do Setor de Oncologia do Hospital de Clínicas Veterinárias da UFRGS entre janeiro de 2015 e dezembro de 2023. Dos 679 animais, fêmeas (59,65%) foram a maioria e houve uma prevalência de animais mais velhos, com média de idade de 9,29 anos. As neoplasias mais comumente encontradas foram linfoma (24,15%), mastocitoma (17,82%) e TVT (16,64%). Linfomas foram mais comuns na forma multicêntrica (91,46%), mastocitomas nas regiões dos membros (30,57%) e tronco (26,45%) e TVT principalmente genital (84,07%). Cães sem raça definida foram a maioria (55,81%) e entre os de raça definida, as mais comuns foram Dachshund (3,97%), Labrador Retriever (3,24%), Boxer (2,95%), Poodle (2,94%), Shih Tzu (2,94%), Rottweiler (2,65%), Pinscher (2,50%), Yorkshire Terrier (2,20%), Pit Bull (1,80%), Golden Retriever (1,62%), Cocker Spaniel (1,62%) Pug (1,62%) e Lhasa Apso (1,50%).
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".