Reduction in the incidence of elbow dysplasia in four breeds of dog as measured by the New Zealand Veterinary Association scoring scheme
Bibliographic record
Abstract
AIM: To determine if there has been any reduction in the incidence of elbow dysplasia in four popular large-dog breeds as measured by the New Zealand Veterinary Association (NZVA) scoring scheme. METHODS: A retrospective analysis of the NZVA elbow dysplasia database was performed using records of all German Shepherd dogs, Labrador Retrievers, Golden Retrievers and Rottweilers that had undergone evaluation since the scheme's inception in 1992. The data for each dog included date of birth, date of radiography, gender, grade of left and right elbow (0, 1, 2 or 3), and accredited or dysplastic status. Ordinal logistic regression was used to model the grade of the worst-affected elbow over time. The model included age at scoring and gender as additional variables. Given the known heritability of elbow dysplasia, the hypothesis was that if the NZVA scheme effectively identifies elbow dysplasia, and that dog breeders have been using the data responsibly, there should have been a trend towards a lower incidence of dogs graded dysplastic over time. RESULTS: In all four breeds, there was a significant trend towards lower grades of the worst-affected elbow over time. For German Shepherd dogs the incidence of elbow dysplasia (worst elbow grade not zero) fell from 75% to 47% between dogs born in 1991 vs those born in 2008. The corresponding figures were a drop from 86% to 68% for the Labrador Retriever, from 89% to 77% for Golden Retrievers, but only 98% to 95% for Rottweilers. In the Rottweiler and Golden Retriever, gender had a significant effect on the worst elbow grade. In the Golden Retriever, age at scoring also had a significant effect. CONCLUSIONS: There has been a significant reduction in the incidence of elbow dysplasia in four popular large-dog breeds as scored by the NZVA elbow dysplasia scoring scheme. The limitations of the study are the non-compulsory nature of the elbow dysplasia scheme, and the potential bias caused by dog breeders or veterinarians pre-screening potential submissions. The results therefore may not represent those of the overall population. CLINICAL RELEVANCE: The incidence of elbow dysplasia, as measured by the NZVA elbow dysplasia scheme, has reduced in the four breeds investigated since the scheme's inception. The New Zealand Kennel Club (NZKC) and the veterinary profession can confidently support the NZVA scoring scheme, and should promote its use by dog breeders.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".