Hereditary nasal parakeratosis in Labrador retrievers: 11 new cases and a retrospective study on the presence of accumulations of serum (‘serum lakes’) in the epidermis of parakeratotic dermatoses and inflamed nasal plana of dogs
Bibliographic record
Abstract
We report 11 new cases of hereditary nasal parakeratosis in Labrador retrievers. The disease was first observed when the dogs were 6 months to 2 years of age, and affected dogs of either sex and all coat colours. Hyperkeratosis and depigmentation were confined to the nasal planum, and affected dogs were otherwise healthy. The principal histological findings in biopsy specimens were marked diffuse parakeratotic hyperkeratosis, multiple intracorneal serum lakes and superficial interstitial-to-interface lymphoplasmacytic dermatitis. Topical applications of propylene glycol in water or white petrolatum were often effective for treatment of the dermatosis. However, continued applications were required to maintain a beneficial response. A retrospective histological study of parakeratotic inflammatory diseases of canine haired skin and inflammatory diseases of the canine nasal planum was performed. The degree of parakeratotic hyperkeratosis and the number and size of intracorneal serum lakes were evaluated. The degree of parakeratotic hyperkeratosis was greater in hereditary nasal parakeratosis specimens than that seen in discoid lupus erythematosus and Malassezia dermatitis. There were more serum lakes in hereditary nasal parakeratosis specimens than in specimens from dogs with discoid lupus erythematosus, Malassezia dermatitis, primary seborrheic dermatitis or zinc-responsive dermatosis. Significant differences in sizes of serum lakes (if present) were not seen.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".