Pelvic Limb Kinetic and Kinematic Analysis in<scp>L</scp>abrador<scp>R</scp>etrievers Predisposed or at a Low Risk for Cranial Cruciate Ligament Disease
Bibliographic record
Abstract
OBJECTIVE: To compare kinematics, net moments, powers, ground and joint reaction forces (GRF, JRF) across the hock, stifle and hip joints in Labrador Retrievers at low risk or predisposed to cranial cruciate ligament disease (CCLD). STUDY DESIGN: Cross-sectional clinical study. ANIMALS: Clinically normal Labradors predisposed (n = 11) or at low risk for CCLD (n = 9). METHODS: Right pelvic limbs were classified as predisposed or not to CCLD using a predictive score equation based on combining tibial plateau angle (TPA) and femoral anteversion angle (FAA) measured on radiographs. Kinematic, GRF, and morphometric data were combined in an inverse dynamics approach to compute hock, stifle and hip kinematics, net moments, powers, and JRF while trotting. RESULTS: The extensor moment of the hock joint was greater in predisposed limbs compared with nonpredisposed limbs (0.37 versus 0.28 Nm/kg). The power generated around the hock and stifle joints in predisposed limbs was increased (0.69 versus 0.44 W/kg for the hock, 1.59 versus 1.05 W/kg for the stifle). Trotting velocity, stance time, vertical and craniocaudal GRF and JRF did not differ between groups. CONCLUSIONS: Extensor moment at the hock was increased in predisposed limbs compared with nonpredisposed limbs. Predisposed limbs generated more energy than nonpredisposed limbs around the hock and stifle joints.
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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 | 0.000 |
| 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) | 0.001 | 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".