Inverse Dynamics Analysis of the Pelvic Limbs in Labrador Retrievers With and Without Cranial Cruciate Ligament Disease
Bibliographic record
Abstract
OBJECTIVES: To quantify net joint moments, joint powers, and joint reaction forces (JRF) across the hock, stifle, and hip joints in Labrador Retrievers with and without cranial cruciate ligament (CCL) disease. To investigate differences in joint mechanics between normal, CCL deficient, and contralateral pelvic limbs. STUDY DESIGN: Cross-sectional clinical study. ANIMALS: Clinically normal (n=14) and CCL-deficient (n=9) Labrador Retrievers. METHODS: Each limb was analyzed separately and classified as normal, CCL-deficient or sound contralateral limb. Kinematic, ground reaction force (GRF), and morphometric data were combined in an inverse dynamic approach to compute hock, stifle and hip net moments, powers and JRF, while trotting. RESULTS: Vertical and braking GRF and JRF were significantly decreased in CCL-deficient limbs. In affected limbs, extensor moments at the hock and hip, flexor moment at the stifle and power in all 3 joints were less than normal. Kinetics also identified a greater joint moment and power of the contralateral limbs compared with normal, particularly of the stifle extensor muscles group. CONCLUSION: Lameness resulting from CCL disease affected predominantly reaction forces during the braking phase and the extension during push-off. A greater contribution of the contralateral limbs to propel the dog forward was identified. CLINICAL RELEVANCE: Reductions in joint motion, loads, and muscle contraction were interpreted as modifications adopted to reduce or avoid painful mobilization of the injured stifle joint. The increased joint moment and power of the contralateral compensating side may correlate with the lameness observed, but also with the predisposition of contralateral limbs to CCL deficiency in dogs.
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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 | 0.000 |
| 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".