Comparison of the Tibial Anatomical-Mechanical Axis Angle and Patellar Positions between Labrador Retrievers and Golden Retrievers with and without Cranial Cruciate Ligament Rupture
Bibliographic record
Abstract
This study investigated proximodistal and craniocaudal patellar positions and assessed these positions with the tibial anatomical-mechanical axis angle (AMA-angle), tibial plateau angle (TPA), relative tibial tuberosity width (rTTW), and Z-angle in Labrador Retrievers (LR) and Golden Retrievers (GR) with and without cranial cruciate ligament rupture (CCLR). Mediolateral radiographs were obtained from 2 groups. The affected group had a normal contralateral stifle measured at the time of unilateral surgically confirmed CCLR, which developed a subsequent contralateral CCLR (SC-CCLR; 40 dogs), and the control group (60 dogs aged >11 years) had normal stifles. In the SC-CCLR group, 95% of the tibial anatomical axes (AAs) were cranial (CR) to the patella, with a median (range) AMA-angle of 2.92° (1.65°-4.92°), while in the control group, 93% of the stifles had AAs caudal (CA) to or in the middle (M) of the patella, with a median (range) AMA-angle of 1.03° (0°-3.52°). The craniocaudal position of the patella was correlated statistically with the AMA-angles (median (range), 0.86° (0-1.61°), 1.87° (1.22-2.7°), and 2.97° (1.72-4.92°) in the CA, M, and CR positions, respectively, but not with other tibial measurements (p<0.0001). The patellar height did not differ between the groups (p<0.0001). The highly significant difference found in the AMA-angle and the craniocaudal patellar position between LR and GR with or without CCL injury suggest that craniocaudal angulation of the proximal tibia could influence the development of canine CCLR.
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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 | 0.002 | 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) | 0.001 | 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".