Comparison of demographic data, disease severity and response to treatment, between dogs with atopic dermatitis and atopic‐like dermatitis: a retrospective study
Bibliographic record
Abstract
BACKGROUND: Limited information is available describing the features of canine atopic-like dermatitis (ALD) compared with atopic dermatitis (AD). OBJECTIVES: To compare demographic data, disease severity and response to therapy between ALD and AD dogs. ANIMALS: Two hundred and fifty-three atopic dogs with intradermal and serum allergen-specific IgE test results were selected retrospectively. METHODS AND MATERIALS: Dogs were enrolled into the ALD group if both IgE tests were negative and into the AD group if at least one test was positive. Demographic data, pruritus level and number of body sites affected before and during therapy, in addition to maintenance therapy protocols, were compared between groups. RESULTS: There were 216 (85.38%) dogs in the AD group and 37 (14.62%) in the ALD group. The soft-coated wheaten terrier, American Staffordshire terrier, English bulldog and Labrador retriever were over-represented in the AD group. No significant differences between the groups were noted regarding the other demographic variables evaluated. There were no differences in the mean pruritus scores and number of affected body sites at the first visit or during treatment. Furthermore, no significant differences between the groups were noted for the maintenance treatment scores and reduction of pruritus level and number of body sites affected during treatment. CONCLUSIONS AND CLINICAL SIGNIFICANCE: The soft-coated wheaten terrier, American Staffordshire terrier, English bulldog and Labrador retriever were over-represented in the AD group. No significant differences in the other demographic data and clinical features were noted between dogs with ALD and AD in the present study.
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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 | 0.001 |
| 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".