Patellar luxation as a complication of surgical intervention for the management of cranial cruciate ligament rupture in dogs
Bibliographic record
Abstract
Summary This retrospective study identified 32 cases of patellar luxation which occurred as a complication of surgical intervention for cranial cruciate ligament rupture (CCLR). The complication was recorded mostly in larger (≥20 kg) dogs with the Labrador Retriever being the most common breed. The complication followed extracapsular, intra-capsular and tibial plateau levelling surgery. The mean time from CCLR surgery to the diagnosis of patellar luxation was 14 weeks. The incidence of patellar luxation occurring as a complication of surgical intervention for CCLR was 0.18% of all CCLR corrective procedures. Corrective surgery for patellar luxation was successful in 79% of stifles. The patellar reluxation rate was significantly lower (p=0.0007) when at least one corrective osteotomy (tibial tuberosity transposition, femoral trochlear sulcoplasty or tibial plateau levelling osteotomy with tibial axial re-alignment) was performed (35%), compared to when corrective osteotomy was not performed (100% patellar reluxation rate). When performing corrective surgery for patellar luxation following CCLR surgery, at least one corrective osteotomy should be performed in order to reduce the patellar reluxation rate. The correction of patellar luxation following surgery for CCLR is challenging and carries a significant rate of failure.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".