Canine atopic dermatitis in the Middle East: clinical signs, signalment and common allergens
Bibliographic record
Abstract
The aim of the study was to examine for the first time the various aspects of canine atopic dermatitis (CAD) presenting in a Middle Eastern country. Medical records of 164 dogs diagnosed with CAD were evaluated. Associations between signalment, lifestyle, clinical signs and allergens were evaluated statistically. Labrador Retriever, German Shepherd dog, Boxer, French Bulldog, Golden Retriever and Shar-Pei breeds were presented more frequently than the regular hospital population (P < 0.0001), and had an earlier disease onset time (P < 0.01). In 22 dogs (13%) signs of CAD were noticed at less than six months of age. Most dogs (75%) lived indoors. Most dogs (83%) had lesions on the ventral part of the body and 68% had foot lesions. After excluding flea allergy dermatitis by implementing strict flea control, 60% of the dogs presented with dorsal distribution of skin lesions. Dogs with ventral lesions were younger when clinical signs first appeared (P < 0.05). Most of the dogs were allergic to dust and/or dust mites (75.6%), with weeds and trees as the next most common allergens. CAD is similar worldwide, but geographic differences may be attributable to genetic pools and allergen loading. This study also shows that early onset of clinical signs, especially in breeds predisposed to CAD and with a dorsal distribution of skin lesions, should not rule out the diagnosis of CAD.
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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) | 0.000 | 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 | 0.001 |
| 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".