Stifle joint osteoarthritis at the time of diagnosis of cranial cruciate ligament injury is higher in Boxers and in dogs weighing more than 35 kilograms
Bibliographic record
Abstract
Osteoarthritis is a ubiquitous disease in dogs. The purpose of this retrospective study was to characterize the severity and distribution of osteoarthritis (OA) within the joint and to identify differences among dog breeds in the severity of OA in the cranial cruciate ligament (CCL)-deficient stifle joint. Radiographs of 240 stifles from 51 Boxers, 66 German Shepherds, 100 Labrador Retrievers, and 23 Siberian Huskies with confirmed CCL rupture were included. Radiographs of the stifle joint were evaluated and OA severity was graded at 33 sites within and around the joint, and patella alta was graded as present or absent for a potential total stifle OA score of 100. Osteophyte size was correlated to OA severity score. Total OA scores were calculated and compared within and between breeds globally as well as at each joint site. Dogs weighing >35 kg had a higher total OA score than those weighing <35 kg. Osteoarthritis scores were highest at the apical patella, proximolateral tibia, and sesamoid bones, corresponding to the proximal, lateral, and caudal aspects of the joint, respectively. No statistically significant differences were found among the mean OA scores of various stifle joint regions. Boxer dogs had a higher total OA score than other breeds. We concluded that dogs have a consistent distribution pattern of OA within the stifle joint after CCL injury. Radiographic OA is more severe in the proximal, lateral, and caudal aspects of the joint. Boxers had more severe OA than the other breeds evaluated in the study.
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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 | 0.001 |
| 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) | 0.002 | 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".