Sebaceous Adenoma Case in a Golden Retriever Dog
Bibliographic record
Abstract
Background: Sebaceous adenoma is a benign tumour originating from the skin's sebaceous glands. These tumours can arise in older and middle-aged dogs, typically on various body parts, head, and extremities. The tumours can manifest as either lumps or ulcers. Ulcers may develop when the animal scratches the tumour mass. Purpose: Increase the insight and ability of veterinary practitioners in diagnosing and providing information regarding the treatment of sebaceous adenomas. Case: A nine-year-old Golden retriever dog was brought to the Bali Veterinary Clinic with a complaint of a lateral wound on the face skin of the right eye. Anamnesis revealed that the initial wound was a lump. Clinical examination showed a yellowish ulcer with a 2 cm diameter. Case Management: A hematology examination indicated mild anemia in the dog, while blood biochemistry revealed no abnormalities. Cytology confirmed that the ulcer was a sebaceous adenoma. The owner declined surgery for their pet, so treatment involved applying Bioplacenton® gel to the ulcer area to prevent further infection and accelerate wound healing. Conclusion: In this case, the ulcer wound can be concluded as a sebaceous adenoma. This tumour is benign and harmless, but it is advisable to remove the tumour mass if it impairs the animal's activity and prevents potential secondary infection.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".