Accuracy and Optimization of Force Platform Gait Analysis in Labradors with Cranial Cruciate Disease Evaluated at a Walking Gait
Bibliographic record
Abstract
OBJECTIVE: To determine the combination of ground reaction forces (GRFs) that best discriminates between lame and non-lame dogs. To compare the sensitivity of force platform gait analysis and visual observation at detecting gait abnormalities in Labradors after surgery for rupture of the cranial cruciate ligament (CCL). ANIMALS: All dogs were adult Labrador Retrievers: 17 free of orthopedic and neurologic abnormalities, 100 with unilateral CCL rupture, and 131 studied 6 months after surgery for unilateral CCL injury, 15 with observable lameness. PROCEDURE: Dogs were walked over a force platform with GRF recorded during the stance phase. Analytic properties of force platform gait analysis were calculated for several combinations of forces. The probability of visual observation detecting a gait abnormality was compared with that of force platform gait analysis. RESULTS: We determined that a combination of peak vertical force (PVF) and falling slope were optimal for discriminating sound and lame Labradors. After surgery, many dogs (75%) with no observable lameness failed to achieve GRFs consistent with sound Labradors. CONCLUSION: A force platform is an accurate method of assessing lameness in Labradors with CCL rupture and is more sensitive than visual observation. Assessing lameness with a combination of GRFs is better than using univariate GRFs. CLINICAL RELEVANCE: Therapies for stifle lameness can be accurately and objectively evaluated using 2 vertical ground reaction forces obtained from a force platform.
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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 | 0.001 | 0.006 |
| 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".