Initial testing of a computer-integrated weight compensation system for rehabilitation of horses
Bibliographic record
Abstract
Management of severely injured or neurologic horses is challenging, with ambulatory support limited to static lifts and rescue slings. The aim of this study was initial testing and adjustment of a novel computer-integrated dynamic lift system, including measuring effects of increasing weight compensation (i.e. load reduction) and time attached to the lift. This automated system was developed to improve outcomes and reduce complications in horses with ambulatory difficulties, allowing for controlled mobility and varying load carried by the horse with independent front and hind limb support. Two healthy Thoroughbred horses were studied using the Anderson rescue sling. The lift was programmed to respond to weight and movement of horses. Weight compensation (% bodyweight) was incrementally increased, for front and hind limbs, to maximum percent tolerated, based on heart/respiratory rates and behavioural scoring. The time attached to the lift was then incrementally increased at maximum tolerated weight compensation previously determined. Measures included heart/respiratory rates, behavioural scoring, muscle enzyme activity and blood flow to distal limbs. Results were analysed descriptively. Avoidance behaviour was observed at front and hind end weight compensation of 18 and 4%, respectively. Average maximum time attached to the lift was 2.25 hours. After 60 minutes, respiratory rate increased >20 breaths (b)/minute, reaching 60 b/minute in one horse and 36 b/minute in the other, with shallow breathing. Other measures remained normal. In conclusion, lift programming was successful for weight compensation and mobility during lift support. Complications included avoidance behaviour and respiratory distress at >20% weight compensation, likely caused by the Anderson rescue sling. To address these limitations, a new rehabilitation harness better suited for long-term use is under development.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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".