Symmetry of hind limb mechanics in orthopedically normal trotting Labrador Retrievers
Bibliographic record
Abstract
OBJECTIVE: To evaluate symmetry of the hind limbs in orthopedically normal trotting dogs. Animals-19 orthopedically normal Labrador Retrievers with no history of lameness. PROCEDURES: Retroreflective markers were applied to the hind limb joints, and a 4-camera kinematic system captured positional data at 200 Hz in tandem with force platform data collection while the dogs trotted. Morphometric data were combined with kinematic and force data in an inverse dynamics method to calculate net joint moments and powers at the joints as well as total support moment for each limb. Dogs were identified as right or left dominant when their total support moment was > 10% asymmetric between sides. RESULTS: 10 of the 19 dogs were mechanically dominant in the right hind limb as determined by their total support moments. One dog was left dominant, and the remaining 8 were symmetric. Right-dominant dogs had larger net joint moments at the right hip, tarsal, and metatarsophalangeal joints and a smaller moment at the right stifle joint, compared with values for the left hind limb. The 1 left-dominant dog had the exact opposite findings. Hip and stifle joint moments and powers varied between limbs of the right-dominant and left-dominant groups in the timing of their transition from negative to positive, and power amplitudes varied at the hip, tarsal, and metatarsophalangeal joints but not the stifle joint. CONCLUSIONS AND CLINICAL RELEVANCE: Sound trotting dogs can have asymmetries in limb and joint mechanics. These natural mechanical asymmetries should be taken into account when considering models to evaluate stresses at joints and when considering surgery for cruciate ligament rupture.
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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 | 0.001 |
| 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".