Canine atopic dermatitis: breed risk in Australia and evidence for a susceptible clade
Bibliographic record
Abstract
BACKGROUND: Genetic studies on canine atopic dermatitis (CAD) indicate that large populations from one geographical location are preferred for the identification of relevant susceptibility genes. Australian dogs are relatively isolated; studies on CAD in this population are limited. HYPOTHESIS/OBJECTIVES: To identify breeds at risk in the Australian dog population and to compare with worldwide breed predisposition. ANIMALS: Case records (n = 23,000) from University Veterinary Teaching Hospital (UVTH) dogs, including 722 with CAD. METHODS: The breed proportion of CAD and odds risk (OR) were calculated. A systematic review of 13 previous studies (1971-2010) was performed and compared to the study results by implementing an atopic dermatitis (AD)-to-reference population ratio (ADRPR). RESULTS: Eleven dog breeds with significant increased OR (≥1.0) were identified; all with breed CAD cases proportionally higher than their base hospital population. Gender risk in males from the pug dog breed (P = 0.007) was detected and the bichon frise breed had a similar trend (P = 0.05). Sixteen predisposed dog breeds were identified by systematic review. All breeds with significant increased OR in UVTH had ADRPR > 1.4; five (boxer, bulldog, Labrador retriever, pug, West Highland white terrier) were recognized as predisposed worldwide. One clade of breeds with common ancestry was highly represented in CAD cases worldwide and in Australia (81% of the significant OR cases). CONCLUSION AND CLINICAL IMPORTANCE: The use of a large population from one geographical location and ADRPR provided an objective comparison between worldwide AD studies; it identified one common clade of susceptible breeds. Breed genetics and related clinical presentation may help CAD diagnosis and treatment.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 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".