Estimated Frequency of the Canine Hyperuricosuria Mutation in Different Dog Breeds
Bibliographic record
Abstract
BACKGROUND: Hyperuricosuria is a condition that predisposes dogs to urate urolithiasis. A mutation that causes canine hyperuricosuria was previously identified in 3 unrelated dog breeds. The occurrence of the mutation in additional breeds was not determined. HYPOTHESIS/OBJECTIVES: Identify additional breeds that have the hyperuricosuria mutation and estimate the mutant allele frequency in those breeds. ANIMALS: Three thousand five hundred and thirty dogs from 127 different breeds were screened for the hyperuricosuria mutation. METHODS: DNA samples were genotyped by pyrosequencing and allele-specific polymerase chain reaction methods. RESULTS: Mutant allele frequencies that range from 0.001 to 0.15 were identified in the American Staffordshire Terrier, Australian Shepherd, German Shepherd Dog, Giant Schnauzer, Parson (Jack) Russell Terrier, Labrador Retriever, Large Munsterlander, Pomeranian, South African Boerboel, and Weimaraner breeds. CONCLUSIONS AND CLINICAL IMPORTANCE: The hyperuricosuria mutation has been identified in several unrelated dog breeds. The mutant allele frequencies vary among breeds and can be used to determine an appropriate breeding plan for each breed. A DNA test is available and may be used by breeders to decrease the mutant allele frequency in breeds that carry the mutation. In addition, veterinarians may use the test as a diagnostic tool to identify the cause of urate urolithiasis.
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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 | 0.002 |
| 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 | 0.000 |
| 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".