Ironing out the wrinkles and folds in the epidemiology of skin fold dermatitis in dog breeds in the UK
Bibliographic record
Abstract
Skin fold dermatitis (intertrigo) is an inflammatory process of closely apposing skin surfaces. Extreme conformations towards folded skin in many dog breeds are linked with higher risk. Using anonymised primary-care veterinary data from the VetCompass Programme, this study aimed to report the frequency, demographic risk factors and clinical management for skin fold dermatitis in the UK. Risk factor analysis used random effects multivariable logistic regression modelling. From a study population of 905,553 dogs, the one-year period prevalence in dogs overall was 0.37% (95% CI 0.35-0.39). Diagnosis was supported by laboratory testing in 4.21% cases. Systemic antibiosis was used in 42.30% cases. Compared with crossbreed dogs, the most highly predisposed breeds were English Bulldog (odds ratio [OR] 49.07, 95% CI 37.79-63.70), French Bulldog (OR 25.92, 95% CI 19.62-34.26,) and Pug (OR 16.27, 95% CI 12.20-21.69). The most protected breeds were Yorkshire Terrier (OR 0.14, 95% CI 0.03-0.56), Border Collie (OR 0.31, 95% CI 0.11-0.84), Jack Russell Terrier (OR 0.53, 95% CI 0.30-0.92) and Labrador Retriever (OR 0.57, 95% CI 0.35-0.93). This study adds further evidence to the welfare concerns around high popularity of dog breeds with extreme conformations. The three breeds with by far the highest odds of skin fold dermatitis represent an extreme brachycephalic conformation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".