Prevalence and genetic parameters for hip dysplasia in Italian population of purebred dogs
Bibliographic record
Abstract
This paper aimed to assess the prevalence of hip dysplasia (HD) in some breeds of dogs widely diffused in Italy and to estimate heritability of HD in German Shepherd and Boxer Italian populations. Data consisted of radiographic findings taken on 32,900 dogs (18,665 females and 14,225 males) of 7 breeds (German Shepherd; Boxer; Labrador Retriever; Golden Retriever; Rottweiler; Dobermann; Cane Corso) screened at an age of 17.9 ± 7.0 months. Radiographs of the coxofemoral joints, taken by 478 veterinarians, were scored for HD grade by a single veterinarian panelist according to a grading procedure based on a 5-class linear system (from A, no signs of dysplasia, to E, severe dysplastic hip changes). Logistic regression analysis was used for studying the relationships between selected explanatory variables with the outcome of the diagnosis for HD. Variance components, direct and maternal heritability have been estimated for German Shepherd and Boxer dogs using a REML animal model procedure. Prevalence of HD (hip joint graded C or worse) for the pool of breeds involved approached 22%, with large differences among breeds. In dogs diagnosed as dysplastic, the mild form (grade C) was largely prevalent for all breeds. When compared to the German Shepherd, the Cane Corso exhibited a significantly higher risk, whereas the Dobermann, Labrador and Rottweiler showed a significantly lower risk of being affected by HD. The probability of being diagnosed as dysplastic increased with the increasing of the age of dogs at screening and with the decreasing of experience of x-raying veterinarians. The effect of birth year of dogs on the outcome of the HD diagnosis was significant, but evidenced an inconsistent trend through years. Heritability estimates approached 0.24 and 0.15 for Boxers and German Shepherds, respectively, whereas maternal heritability was close to 0.03 for both breeds. Results from this study demonstrated that HD is fairly prevalent in some breeds of dogs commonly found in Italy, and its reduction should be a goal in breeding schemes of purebred dogs. Age at screening and experience of the x-raying veterinarians are disturbance factors to be considered in screening programs for HD. Heritability estimates for HD was low, but additive genetic variance seems enough for conjecturing selection programs aimed to decrease hip joints disease. Given the low heritability values, current selection schemes based on phenotypic records seem ineffective, whereas the use of breeding values estimated under BLUP animal model procedures should be recommended for gaining genetic progress of Italian dog populations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".