Inheritance of Monogenic Hereditary Skin Disease and Related Canine Breeds
Bibliographic record
Abstract
The plasticity of the genome is an evolutionary factor in all animal species, including canines, but it can also be the origin of diseases caused by hereditary genetic mutation. Genetic changes, or mutations, that give rise to a pathology in most cases result from recessive alleles that are normally found with minority allelic frequency. The use of genetic improvement increases the consanguinity within canine breeds and, on many occasions, also increases the frequency of these recessive alleles, increasing the prevalence of these pathologies. This prevalence has been known for a long time, but mutations differ according to the canine breed. These genetic diseases, including skin diseases, or genodermatosis, which is narrowly defined as monogenic hereditary dermatosis. In this review, we focus on genodermatosis sensu estricto, i.e., monogenic, and hereditary dermatosis, in addition to the clinical features, diagnosis, pathogeny, and treatment. Specifically, this review analyzes epidermolytic and non-epidermolytic ichthyosis, junctional epidermolysis bullosa, nasal parakeratosis, mucinosis, dermoid sinus, among others, in canine breeds, such as Golden Retriever, German Pointer, Australian Shepherd, American Bulldog, Great Dane, Jack Russell Terrier, Labrador Retriever, Shar-Pei, and Rhodesian Ridgeback.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".