Biomechanical comparison of standing posture and during trot between German shepherd and Labrador retriever dogs
Bibliographic record
Abstract
It is widely accepted that canine breeds stand and move differently. The prevalence of various musculoskeletal disorders such as hip and elbow dysplasia is also different between breeds. German shepherd dog (GSD) and Labrador retriever dog (LRD) are two large breeds with different conformations that have high prevalence of these disorders. This study quantifies the movement and standing posture of twelve healthy GSDs and twelve healthy LRDs to identify biomechanical similarities and differences that may be linked to sub-optimal hip and elbow mechanics. A pressure walkway and a motion capture system obtained measures of kinetics, kinematics and conformation during standing and trot. During standing, LRDs carry a greater percentage of the weight on the forelimbs (69%±5% vs. GSDs: 62%±2%, p<0.001) and their body Centre of Pressure (CoP) is located more cranially (p<0.001). GSDs had a greater pelvic tilt (79°±8 vs. 66°±9°, p = 0.004), more flexed stifles (44°±9° vs. LRDs: 34°±10°, p<0.05) and hocks (58°±11° vs. 26°±9°, p<0.01) and more extended hips (-10°±11° vs. 30°±12°, p<0.001). During trot, the GSDs' CoP had a longer anterior-posterior trajectory (151%±22% vs. LRDs: 93%±25% of the withers height, p<0.001). Stride parameters and loading of limbs were similar when normalised to the size and weight of the dog, respectively. The LRDs had a more extended thoracolumbar angle (p<0.001) and a less flexed lumbosacral angle (p<0.05). The LRDs' hip remained flexed during trot whereas the GSDs' hip joint was less flexed during swing (p<0.001) and more extended in late stance and early swing (p<0.001). In conclusion, the LRDs and GSDs differ in the way they stand and move and this would result in different loading pattern of the joints. Further investigation is required to determine the extent to which biomechanical differences are linked to musculoskeletal problems presented clinically.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".