PSXI-6 Genetic analysis of hip and elbow dysplasia in guide dogs.
Bibliographic record
Abstract
Abstract Diseases such as canine hip (HD) and elbow (ED) dysplasia are common orthopedic conditions that have significant effects on the health and welfare of many dog breeds and are an ongoing issue in veterinary medicine. These conditions are more prevalent in large breeds, such as ones used as service dogs. Currently, trained vetrinarians score HD and ED using X-ray imaging as a phenotype for selecting service dogs. However, integrating both traits into a genetic evaluation and selecting against dysplatic joints opens up the possibility to reduce their prevalence in the population and accelerate the improvement of canine hip and elbow health. The objective of this study is to investigate the genetic parameters of HD and ED scores, as a first step to understand their underlying genetic mechanisms. The data for this study consisted of both HD and ED scores of over 3,500 guide dogs in training, collected between 2010 and 2024, including Poodles, Labrador Retrievers, Golden Retrievers, and Labrador-Golden Retriever crosses. After data processing, univariate and multivariate within-breed animal models were developed and implemented in ASREML 4.2. This study highlights the first step into understanding the underlying genetic mechanisms of two important health traits, ED and HD, for Canadian service dogs to begin the process of routine genetic improvement to increase the livability and overall quality of life of service dogs.
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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 | 0.003 | 0.001 |
| 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) | 0.002 | 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".