Evaluation of a treadmill with integrated force plates for kinetic gait analysis of sound and lame dogs at a trot
Bibliographic record
Abstract
The objective was to compare mean peak vertical force (PVF) obtained with a treadmill with two integrated force plates (TM) with the piezoelectric force platform (FP) for sound and lame dogs at a trot. The aim was also to report the inter-step variability (ISV) for both systems and the effect of lameness on these values. Six sound dogs (20.0-25.5 kg) and six dogs with a grade 2/5 forelimb lameness (17.0-36.1 kg) were used in the study. Dogs were acclimatized and assigned an individual target velocity (1.8-2.2 m/s). Mean PVF measurements were obtained for both TM and FP. Subject velocity was controlled by belt speed on TM and restricted to 0.25 M/s above or below the assigned target velocity for FP. Acceleration was limited to +/- 0.3 M/s2. For the sound dogs, concordance and correlation coefficients of the mean PVF for the front limbs was 0.79 and 0.76, respectively. Concordance and correlation for the rear limbs was 0.90 and 0.81, respectively. For the lame dogs, concordance and correlation for the front limbs was 0.73 and 0.59, respectively. Concordance and correlation for the rear limbs was 0.89 and 0.95, respectively. ISV was 0.94 with TM and 0.84 with FP for the sound dogs and 0.96 with TM and 0.87 with FP for the lame dogs. In conclusion, TM provided rapid PVF measurements, good concordance for the hind limbs, and substantial concordance for the forelimbs in both sound and lame dogs at a trot as compared to FP. Both systems demonstrated excellent ISV for both lame and sound 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".