The Prevalence and Risk Factors of Contralateral Cranial Cruciate Ligament Rupture in Medium-to-Large (≥15kg) Breed Dogs 8 Years of Age or Older
Bibliographic record
Abstract
Abstract Objective The aim of this study was to determine the prevalence of contralateral cranial cruciate ligament rupture (CCLR) in dogs 8 years of age or older, weighing more than or equal to 15 kg at the time of first-side CCLR and to assess associated risks. Study Design It is a cross-sectional retrospective study of 831 client-owned dogs Methods Medical records of dogs weighing more than or equal to 15 kgs that were more than or equal to 8 years of age at the time of first CCLR diagnosis were reviewed. Data collected included weight, sex, pre-operative tibial plateau angle, co-morbidities, time between diagnosis of first CCLR and diagnosis of contralateral CCLR. Multivariate logistic regression analysis was used to estimate odds ratio. A median follow-up period of over 112.7 months (25th/75th quartiles 75.4/157.7 months) from first CCLR diagnosis was allotted. Results Eight-hundred thirty-one dogs were identified and included. About 19.1% (159/831 dogs, 95% confidence interval: 16.6–22.0%) of dogs that experience a first-side CCLR at 8 years of age or older will rupture the contralateral side, a median of 12.9 months (25th/75th quartiles 6.5/24.3 months) later. Age (p = 0.003) and breed, Golden Retrievers (p = 0.028) and Labrador Retrievers (p = 0.007), were factors significantly associated with contralateral CCLR. Clinical Relevance The prevalence of contralateral CCLR in medium-to-large breed dogs more than or equal to 8 years of age old is less than previously reported and the risk decreases as they age. This important information will help guide owners when deciding to pursue surgical stifle stabilization following CCLR in older dogs.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".