Determination of allergens involved in canine atopic dermatitis in Bosnia and Herzegovina
Bibliographic record
Abstract
Abstract Canine atopic dermatitis (CAD) is a common skin disease and numerous factors participate in forming clinical features of this disease. Intradermal tests (IDT) enabled determination of allergen(s) involved in CAD. Allergens that can be related with CAD are numerous and depend on geographical region. The purpose of this study was to identify the most frequent allergen(s) associated with CAD to which dogs with atopic dermatitis (AD) most often react with hypersensitive reaction. IDT were performed with 15 allergens on fifty dogs with clinical signs of AD. Mixed breed (n= 10), Pekingese (n= 9), Labrador Retriever (n= 6) and American Staffordshire Terriers (n= 5) were the most common breeds among 50 tested dogs. The majority of dogs showed clinical signs of AD at age of less than three years. Clinical signs appeared seasonally in the spring and summer. Pruritus was present in 74% cases. Polysensitization was noted in 96% of tested dogs, while 4% of tested dogs were negative to used allergens. The highest percentage of allergen positive reactions was to house dust (78%,) and house dust mite (68%) (p<0.01, respectively). Key words: dogs, atopic dermatitis, allergens, IDT
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".