Development of a Front Limb Support to Facilitate Weight Reduction in Horses with Ambulatory Difficulties
Bibliographic record
Abstract
Abstract In horses, severe limb injuries and other problems affecting ambulation are challenging to manage. Offloading the injured limb can result in secondary complications such as supporting limb laminitis (SLL), severely affecting quality of life and sometimes necessitating euthanasia. SLL results from increased load and decreased blood flow to the foot. There is a need to develop a dynamic device to reduce the load on the limbs while maintaining mobility and blood flow for the rehabilitation of horses with ambulatory difficulties. In this study, the unique biomechanics of the horse were considered in the design of a dynamic front limb weight support system. The development further had to consider complications associated with its use, such as pressure ulcers and other tissue trauma. Therefore, the design included silicone air pockets to be inflated and deflated in a programmable cycle. A series of three prototypes resulted in a front limb support (breastplate) intended for use with a computer-controlled rehabilitation lift. Iterative design modifications of the breastplate allowed to safely provide up to 50% front limb weight reduction while maintaining horse comfort. This is a significant step towards adjustable, dynamic ambulatory support with the ability to customize rehabilitation programs for horses with ambulatory difficulties.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".