Assessment of a continuous passive motion assistive device in dogs following stifle surgery
Bibliographic record
Abstract
Canine rehabilitation optimizes recovery and the quality of life in dogs with musculoskeletal conditions or after surgery. Achieving proper range of motion (ROM) is vital post-stifle surgery, often accomplished through manual therapy and active exercises. We investigated the mechanical performance of a continuous passive motion (CPM) device for dogs and its potential use in canine rehabilitation therapy. In the ethical review process, our research was accepted to be evaluated in a sample of four dogs that had undergone left stifle surgery. Each dog underwent four sessions with the device at three different speeds. Results showed the device replicated extension angles close to goniometer measurements used in manual therapy. Flexion was also achieved, but not to the same extent. A force threshold stopped the device, avoiding discomfort in dogs with restricted ROM. Dog-specific factors like body position, opposition to movement, limb size, stage of recovery, haircoat, and discomfort, appeared to influence device operation. Mechanical improvements to allow for enhanced flexion are recommended in future CPM device designs, including a resistance threshold that could be adjusted for individual dogs and stages of healing. This study serves as a foundation for future advancements in canine rehabilitation systems. A canine CPM device may provide an affordable option to improve ROM. This could be beneficial for dog owners, who may not be comfortable with manual therapy, to assist with home rehabilitation exercises.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".