Acral Lick Dermatitis (Lick granuloma) in an Adult Male Labrador Retriever Dog
Bibliographic record
Abstract
Introduction: Acral lick dermatitis is a skin injury commonly noticed in dogs with obsessive licking behavior. The lesions are usually noticed on the distal extremities which become raised, thickened, and plaque-like. Case report: A five-year-old male Labrador retriever dog was presented to the Small Animal Medicine Unit of Veterinary Clinical Complex (VCC), Rajiv Gandhi Institute of Veterinary Education and Research (RIVER), Puducherry, India, with a history of a superficial wound on the metatarsal region of the right hind limb with bleeding and continuous licking since a month. Clinical examination of the lesion showed a nodular eczematous lesion of 2 cm thickness, while other vital parameters were normal. Based on the licking behavior and other investigations, the skin lesions were diagnosed as acral lick dermatitis. Treatment included the application of Ointment Triamcinolone acetonide (topically) for a month. The licking was controlled using E-collar, and the dog was engaged in playful activities to overcome boredom. The lesion regressed completely within a month and hence was treated uneventfully. Conclusion: Diagnosis and identifying the root cause of the skin disorder can determine the course of treatment. Topical application of corticosteroids (triamcinolone acetonide) and methods, such as E-collar, to control the licking behavior, helped the animal’s recovery.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".