Age of neutering contributes to risk of cruciate ligament rupture in Labrador Retrievers
Bibliographic record
Abstract
OBJECTIVE: Cruciate ligament rupture (CR) in Labrador Retrievers is a complex polygenic disease with high heritability. The environmental contribution to CR risk remains poorly characterized. An accurate genetic risk test for CR in the Labrador Retriever has been developed. This enables evaluation of environmental risk with knowledge of genetic disease predisposition through study of dogs with phenotypic disease status that is discordant with their genetic risk. The objective of this study was to identify environmental factors that contribute to CR in Labrador Retrievers through evaluation of dogs with clinical phenotypes that are discordantly predicted with the use of genetic markers. METHODS: Dogs were prospectively recruited between January 2013 and December 2022. To study discordant subjects, case dogs with a posterior risk probability value < 0.75 and control dogs with a posterior risk probability of > 0.25, determined with the use of an average of 8 statistical models, were selected. The environmental factors investigated were neuter status, age of neuter, withers height measured at the dorsal-most ridge between the scapulae, weight, body mass index, and athletic activity. RESULTS: Ninety three dogs were discordant: 58 dogs were discordant CR cases, and 35 dogs were discordant CR controls. Neutering before 12 months of age was a significant risk factor for CR development. Sex, neuter status, or status as an athlete was not associated with CR risk. CONCLUSIONS: Neutering before 12 months of age influences risk of CR in Labrador Retrievers. CLINICAL RELEVANCE: This information can inform management decisions about Labrador Retrievers regarding age of neutering, body condition, and athletic activity. The primary factor influencing CR development in Labrador Retrievers is polygenic intrinsic genetic risk.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".