Die Rolle der Umwelt bei der Entstehung der caninen atopischen Dermatitis
Bibliographic record
Abstract
Canine and human atopic dermatitis (AD) are multifaceted diseases which clinical development may be influenced by several factors such as genetic background, environment, secondary infections, food and psychological effects. The role of the environment has been extensively examined in humans but remains unclear in dogs. The aim of the present study is to examine environmental factors in 2 genetically close breeds: Labrador and Golden Retrievers. Using standard criteria, atopic dogs were selected and compared to healthy individuals. Information on environmental factors was collected using a questionnaire. Univariate and multivariable logistic regression was subsequently used in order to assess the association between all potential risk factors and the disease status. The following parameters, resulting from the multivariable logistic regression, were associated with an increased risk of disease development: living in a shed during puppyhood, adoption at the age of 8 to 12 weeks and washing the dog regularly. On the contrary, the following factors were associated with a lower risk: living in a rural environment, living in a household with other animals and walking in the forest. These associations do not prove causality but support the primary hypothesis that certain environmental factors may influence canine AD development. Further studies are warranted to confirm the current results and conclusions.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".