Design and evaluation of an offloading orthosis for medial knee osteoarthritis
Bibliographic record
Abstract
Knee osteoarthritis is an incurable degenerative joint disease that affects millions of people. Characterized by stiffness and knee pain in the early stages, it can cause loss of function and mobility. Most treatment options are either not sustainable, invasive, or costly. Alternatively, offloading knee orthoses are a cost-effective option that provides immediate pain relief when worn. Offloading knee orthoses, however, have low patient compliance rates. To improve patient compliance and optimize patient benefit, current orthosis designs must be enhanced to improve comfort, increase the perceived effect, and be adjustable to the patient. Consequently, this research presents the design, fabrication, and testing of a new offloading knee orthosis joint. The novel modular orthosis features an offloading mechanism intended to relieve the load on the joint solely during stance phase and reduce the moment during swing phase when offloading is not needed. Three-point bending tests of the experimental prototype demonstrated an offloading moment of 3.36 Nm, creating a noticeable offloading effect during stance, and reduced the moment to less than 0.5 Nm after 35° of knee flexion, thus, potentially increasing comfort during swing phase and sitting when offloading forces are not needed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".