Dog factor differences in Can f 1 allergen production
Bibliographic record
Abstract
BACKGROUND: The clinical importance of dog allergy is well known, but it is unknown if all types of dogs represent the same risk for allergic patients. The purpose of this work was to evaluate among 288 healthy dogs if the levels of Can f 1 on fur vary between breeds (German Shepherd, Pyrenean Shepherd, Poodle, Cocker spaniel, Spaniel, Griffon, Labrador retriever and Yorkshire terrier), gender, hormonal status, hair length, and according to the presence of seborrhea. METHODS: Each dog was shaved in a limited area and Can f 1 concentrations were measured in mug/g fur by ELISA. The results (geometric mean values and 95% confidence intervals) were analyzed using analysis of variance and with nonparametric tests. RESULTS: A wide variability in Can f 1 levels was found between dog breeds, from Labradors [1.99 (0.03-129.91)] to Yorkshires [16.72 (3.67-76.16)] and Poodles [17.04 (2.79-103.94)] but only the Labrador levels were significantly different from each other breed. Males produced more Can f 1 than females, 11.75 (1.27-108.40) vs 8.89 (0.91-86.39). No difference was found according to hair length or hormonal status. The seborrheic status highly (P = 0.0019) influenced the presence of Can f 1 on hair: 16.66 (1.59-173.96) vs 9.40 (1.03-85.70). CONCLUSION: Breeds (Labrador retriever), sex and seborrhea seem to influence the levels of Can f 1 on fur.
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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 | 0.000 | 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) | 0.003 | 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".