A practical comparison of three objective gait analysis systems in a diverse population of horses
Bibliographic record
Abstract
Objective gait analysis systems can supplement veterinary lameness exams, but the agreement of many available systems is poorly understood. This study aims to compare the data from three commercially available systems across a diverse horse population with lameness originating from multiple limbs, to help guide clinical interpretation. A body-worn inertial measurement unit system (IMUS), an artificial intelligence app (AIA), and pressure sensing boots (PSB) were compared. Results from the three systems were analyzed to determine which limb each system reported as responsible for the most asymmetric movement. Comparing the AIA and IMUS in 31 horses, the two systems agreed on the limb resulting in the most asymmetrical movement for 87.1% of the population. For a subset (n=23) also equipped with the PSB, the IMUS and PSB agreed for 26.1% and the AIA and PSB agreed for 34.8% of the population. Objective gait analysis systems have the potential to be useful in aiding clinicians for both diagnosing and monitoring the treatment of musculoskeletal injuries. In cases of complex movement, mixed and inconsistent lameness presentations may create difficulties for both clinicians and objective gait systems to differentiate the movement results. When assessing cases of multi-limb lameness and/or complex movement patterns, collecting additional strides may be the best practice for the objective gait analysis systems to provide more consistent results. The authors concluded that the AIA and IMUS had comparable results when evaluating upper body kinematics in a diverse population of horses and the PSB needs further validation before more comparisons can be conducted.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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".