Update of a Retrospective Cohort Study of Changes in Hip Joint Phenotype of Dogs Evaluated by the OFA in the United States, 1989–2003
Bibliographic record
Abstract
OBJECTIVE: To determine whether there has been improvement in canine hip joint phenotype classifications of dogs whelped from 1989 to 2003 by the Orthopedic Foundation for Animals (OFA), by examining results of radiographic evaluations and identifying any trends in percentages of dogs classified as having desirable hip joint phenotypes. STUDY DESIGN: Retrospective cohort study. SAMPLE POPULATION: OFA radiographic classifications (n=431,483) on dogs whelped between 1989 and 2003. METHODS: Numbers and percentages of dogs classified by hip joint phenotypes were determined for 2-year cohorts. Differences between breeds and sexes were assessed using the Fisher's exact test, and odds ratios with 95% confidence intervals were calculated to express associations. The Cochran-Armitage test for trend was calculated to identify significant trends over time. RESULTS: There were statistically significant (P<.05) increases in the proportion of all breeds of dogs evaluated as excellent and good from 1993 to 2003, controlling for gender and age at evaluation. Labrador Retrievers, Bernese Mountain Dogs, and Rottweilers had the highest proportions of excellent and good scores, and the highest rates of improvement in excellent and good scores were seen in Bernese Mountain Dogs and Rottweilers. CONCLUSIONS: Results support the contention that there have been improvements in hip joint phenotype classifications in dogs in the United States since the previous study (1989-1992), through increases in the proportion of dogs receiving excellent and good classifications. CLINICAL RELEVANCE: Hip joint phenotype classifications can be used by dog breeders to develop breeding programs to improve the hip joints of future generations of dogs.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".