Recherche de biomarqueurs précoces du développement de la dermatite atopique chez des chiens de races prédisposées
Bibliographic record
Abstract
Atopic dermatitis, an inflammatory, pruritic, and chronic dermatosis, affects both dogs and humans. It is caused by a defect in the skin barrier and leads to eczema lesions that worsen over time. Early management is crucial for effective treatment.In this context, early diagnosis is particularly valuable. In humans, it has been shown that skin hydration levels and transepidermal water loss at the age of three months are predictive of eczema lesions at six months of age.Certain dog breeds are predisposed to the disease, including the Golden Retriever and the Labrador Retriever, which are both widely represented in the canine population, particularly among assistance dogs.The BIOMAD study is a longitudinal follow-up of a cohort of dogs from the Handi’chiens association and dogs owned by veterinary students. This pilot study allows the monitoring of three skin biomarkers in healthy dogs: pH, hydration level, and transepidermal water loss. Additionally, differences in these values are being investigated by comparing healthy dogs with those suspected of having atopic dermatitis. So far, no significant difference has been identified, mainly due to the very small number of suspected cases, but the continuation of the project will allow the recruitment of more subjects to pursue this objective.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".