Water height modifies forelimb kinematics of horses during water treadmill exercise
Bibliographic record
Abstract
Although equine water treadmills (WTs) are increasingly being used for the rehabilitation of equine athletes, the immediate- and short-term effects of this type of exercise on limb movement in the WT and overground are unknown. Therefore, the objective of this study was to evaluate the effects of WT exercise on equine forelimb kinematics on land and under various WT conditions before and after a prolonged period of WT conditioning. External markers were used to collect 2D kinematic data (joint extension, flexion, range of motion (ROM), elevation; stride mechanics) at 120 frames/s. Thirteen mixed breed, English performance horses were filmed under three test conditions: (1) horses walked on sand; (2) horses walked on a WT (water heights: dry, carpus, stifle); (3) horses walked on sand immediately following a WT session. Walking speeds during testing varied between horses, ranging from 1.3-1.5 m/s, but were held constant for each individual horse for all tests on both days. Testing occurred on days 1 and 10, with horses being trained on the WT for days 2 through 8 (28 min/day). Comparisons were made using linear mixed effects models. Carpal ROM and elbow ROM were greater when horses were walked in water, compared to without water (dry treadmill) and overground (P<0.0001 for all). This increased ROM was achieved primarily via an increase in joint flexion. With the addition of water, stride length increased and stride frequency decreased, accompanied by an increased percentage duration of swing phase. No effects of conditioning on gait mechanics were observed overground. Therefore, WT exercise may be meaningful for physical rehabilitation through increased joint flexion and ROM observed.
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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 | 0.000 |
| 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) | 0.001 | 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".