Patellar luxation in 70 large breed dogs
Bibliographic record
Abstract
OBJECTIVES: To report the signalment, history, clinical features, and outcome in dogs weighing greater than 15 kg, treated surgically and non-surgically for patellar luxation. Risk factors for the development of patellar luxation, postoperative complications, and outcome were evaluated. METHODS: Details regarding signalment, bodyweight, breed, aetiology, unilateral or bilateral luxation, duration of lameness, grade of luxation, direction of luxation, grade of lameness at presentation, concomitant cranial cruciate ligament rupture, method of treatment, surgical technique, surgeon, and complications were obtained from the medical records. Outcome was graded as excellent, good, fair, or poor, according to the degree of lameness. RESULTS: Seventy dogs (45 males and 25 females) were included. Thirty-five had bilateral luxations (105 limbs). Mean age was two years, and mean weight was 30 kg. The relative risk for Labrador retrievers was 3.3 (P<0.001). All luxations were developmental. Luxations were medial in 102 stifles and lateral in three. Fourteen stifles had concomitant cranial cruciate ligament rupture. As the grade of patellar luxation increased, so did the grade of lameness (P<0.001). Surgery was performed in 70 stifles, and outcome was excellent/good in 94 per cent and fair/poor in 6 per cent of stifles. Complications occurred in 29 per cent of stifles, and increasing bodyweight was found to be a risk factor (P=0.03). Thirty-five stifles were managed non-surgically, and outcome was excellent/good in 86 per cent and fair/poor in 14 per cent of stifles. CLINICAL SIGNIFICANCE: In view of the potential risk of postoperative complications, all surgically treated cases of patellar luxation in large breed dogs should be managed with a femoral trochleoplasty, a tibial tuberosity transposition (stabilised with K-wires and a tension band wire), and soft tissue releasing and tightening procedures.
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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 | 0.001 |
| 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".