Environmental risk factors for canine atopic dermatitis: a retrospective large‐scale study in Labrador and golden retrievers
Bibliographic record
Abstract
BACKGROUND: Canine atopic dermatitis (cAD) is one the most common and distressing skin disorders seen in dogs. It is characterized by dysfunction in the skin barrier, with a complex pathogenesis combining both genetic and environmental factors. OBJECTIVES: To evaluate associations between environmental factors and case-control status in two closely related, at-risk breeds, the Labrador retriever and golden retriever. ANIMALS: Two thousand four hundred and forty-five pet dogs, of which 793 were classed as cases (575 Labrador and 218 golden retrievers) and 1,652 as controls (1,120 Labrador and 532 golden retrievers). METHODS AND MATERIALS: Case-control status was assigned based upon owner response to a standardized validated questionnaire. Retrospective data on rearing environment were collected via additional questions. Univariate and multivariate logistic regressions were utilized to evaluate associations between environmental factors and case-control status. RESULTS: Risk factors included being reared in an urban environment (not living currently in an urban environment), being male, being neutered, receiving flea control and being allowed on upholstered furniture. Protective factors included living with other dogs (not cats) and walking in woodlands, fields or beaches. Additionally, amongst Labrador retrievers, chocolate-coloured dogs were at greater risk of having cAD than black- or yellow-coated dogs. CONCLUSIONS AND CLINICAL IMPORTANCE: This study is the largest of its kind to date to investigate the role of the environment in cAD. Although precise triggers are unclear, this study complements earlier studies in highlighting the protective role of a rural environment and some novel associations with disease development.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".