Seasonal variation in the hip score of dogs as assessed by the New Zealand Veterinary Association Hip Dysplasia scheme
Bibliographic record
Abstract
AIM: To determine whether there is a seasonal variation in the phenotypic hip score of dogs born in New Zealand as assessed by the New Zealand Veterinary Association (NZVA) canine hip dysplasia (CHD) scheme. METHODS: Data from dogs born in New Zealand between 1988 and 2009 that have been scored for CHD were retrospectively evaluated for the effect of month of birth on radiographic phenotype. Data included both the total score and the subtotal score, comprising Norberg's angle, the subluxation score and changes to the cranial acetabular edge, for each dog. Datasets were created for all breeds combined and for the four most populous breeds using the scheme (German Shepherd dog, Labrador Retriever, Golden Retriever and Rottweiler) and stratified according to month of birth and season. Due to the skewed nature of the data, a Kruskal-Wallis Rank Sum test was used to test for statistical significance. Additionally, χ² analysis was performed using the median of each dataset (proportion above/below the median). The null hypothesis was that there would be no effect of month of birth, and hence seasonality, on hip phenotype for dogs born and scored in New Zealand by the NZVA. RESULTS: For all breeds combined, month of birth had an effect on total and subtotal NZVA CHD scores (p<0.001) with a lower total hip score in the autumn months of March and April than other months. When individual large breed data were analysed, there was an effect of month of birth on total and subtotal scores for the Labrador Retriever and the Rottweiler (p ≤ 0.05), but not the German Shepherd dog or Golden Retriever breeds. CONCLUSIONS: Being born in the autumn was associated with a protective effect on hip phenotype in some breeds. These results suggest that weather and/or another seasonal factor may have a significant environmental effect on the phenotype of the coxofemoral joint. CLINICAL RELEVANCE: The protective effect of being born in autumn suggests that a decreased level of exercise during subsequent development over winter may positively impact on final coxofemoral joint conformation. Whilst statistically significant, the magnitude of the sparing effect is not likely to be clinically relevant. However, this study, in concert with other studies, may suggest that the effects of exercise can be manipulated to improve hip phenotype.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.001 |
| 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".