Retrospective epidemiological study of skin diseases in dogs and cats
Bibliographic record
Abstract
Dermatology is definitely one of the most important specialties in small animal veterinary medicine, as the skin is the largest organ of the body, it is exposed to the environment, therefore possibility to be affected is high and it is also an indicator of the animal’s general health. As the majority of the dermatological cases are complicated, in many of them, there is need of complementary exams in the form of biopsy and histopathological findings. Skin biopsy is getting more and more importance in the field of veterinary dermatology. In this work is presented the importance of skin biopsy, indications and contraindications of the technique, detailed explanation of the skin biopsy technique, critical points, as well as shipment of the material to laboratory. It also explains the role and importance of a pathologist for the clinical cases, as well as brief histopathological terminology used in the results. A retrospective epidemiological study is presented, based on the results of the skin biopsies of dogs and cats, obtained from INNO laboratory in Portugal between years 2010- 2016. Main goal of this study is to gain information about the final diagnosis based on underwent skin biopsies, and to gain knowledge about the most common dermatological diseases of pets in Portugal. The majority of the biopsies received came from male dogs, with an average age of 7,3 years. The 5 most affected breeds are Labrador Retriever, Boxer, German Shepherd, Cocker Spaniel and undetermined breed. In total, 74 different types of breeds were present. The 3 most common diseases, which dogs suffer from are neoplastic, allergic and infectious diseases. Considering cats, females are more predisposed to suffer from dermatological diseases. The average age of affected cats is 7,5 years. The 2 most affected breeds are European shorthair cat and undetermined breed. In total, skin biopsies were obtained from 5 different types of breeds. The 3 most common diseases in cats are allergic, neoplastic and infectious diseases.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".