A Candidate Gene Study of Canine Joint Diseases
Bibliographic record
Abstract
Canine osteoarthritis (OA) commonly occurs in association with articular diseases, such as hip dysplasia (HD), elbow dysplasia (ED), or cranial cruciate ligament rupture (CCLR). We hypothesized that a common genomic risk for the development of canine joint disease and canine OA would be identified by evaluating the allele frequencies of candidate gene single nucleotide polymorphisms (SNPs) in dogs with OA associated with different articular diseases when compared with a general population of breed-matched dogs. DNA was extracted from blood samples obtained from Labrador Retrievers and Golden Retrievers surgically treated for ED, HD, and CCLR and confirmed to have radiographic evidence of OA. One hundred and thirteen SNPs in 20 candidate genes were genotyped. No significant associations were identified for SNPs or haplotypes in the candidate genes for the diseases evaluated. The candidate gene approach for the study of genetic association is unlikely to be successful for complex canine diseases such as OA without prior trait mapping evaluation.
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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 | 0.000 | 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".