Estimation of genetic population variables for six radiographic criteria of hip dysplasia in a colony of Labrador Retrievers
Bibliographic record
Abstract
OBJECTIVE: To estimate genetic population variables for 6 radiographic criteria of canine hip dysplasia (CHD). ANIMALS: 664 full- and half-siblings from a colony of Labrador Retrievers. PROCEDURE: Heritability estimates and genetic correlations were calculated for 6 radiographic criteria of CHD. Two evaluation protocols were compared: the grade of the most severely affected hip joint and the sum of the scores for both hip joints. The predictive performance of estimated breeding values was also evaluated. RESULTS: The overall prevalence of CHD (Federation Cynologique Internationale grades C, D, and E) was 29.6%. Median age at radiographic examination was 377 days. Heritability for the total CHD grade, Norberg angle (NA), coverage of the femoral head (COV), craniodorsal acetabular rim (ACR), subchondral bone sclerosis (SUBCH), shape of the femoral head and neck (FHN), and osteoarthritic changes at the insertion site of the joint capsule (JC) was estimated as follows: 0.44, 0.43, 0.46, 0.37, 0.32, 0.21, and 0.05, respectively. Heritability estimates were slightly higher for the sum of the scores for both hip joints. If NA and COV were included as fixed effects in the model for the dependent variables ACR, SUBCH, FHN, and JC , then heritability of these traits significantly decreased (0.08 to 0.15). High scores of NA and COV lead to a significant increase of the scores of the remaining criteria. CONCLUSIONS AND CLINICAL RELEVANCE: Canine hip dysplasia is heritable to a moderate degree. Signs of subluxation revealed the highest heritability estimates. The criteria ACR, SUBCH, FHN, and JC were strongly influenced by NA and COV.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".