Effects of simple knee sleeves on pain and knee adduction moment in early unilateral knee osteoarthritis
Bibliographic record
Abstract
Knee sleeves are often prescribed to alleviate pain in people with early knee osteoarthritis. However, the biomechanics underlying their pain-relieving effect are still not well understood. This pre–post study aims at evaluating and comparing the effects of two different types of knee sleeves on knee adduction moment. Patients with clinically diagnosed knee osteoarthritis were recruited from the University of Malaya Medical Centre and were randomly assigned to two test groups using (1) a simple knee sleeve and (2) a simple sleeve with patella cutout. Knee adduction moment was collected using the Vicon motion capture system with two Kistler force plates. Pain, stiffness and physical functions were recorded using the Western Ontario and McMaster Universities Osteoarthritis Index. All measurements were taken before, immediately after and at the completion of 6 weeks of application (primary time point). In total, 17 participants with early unilateral knee osteoarthritis (47.7 (9.7) years) completed the study. Overall results show significant reduction in pain, early stance and late stance knee adduction moment and increased walking speed after 6 weeks of both knee sleeves application. This study results suggest that knee sleeves can reduce knee adduction moments in early unilateral knee osteoarthritis by 14.0% and 12.1% using the simple sleeve and the sleeve with patella cutout, respectively, and can potentially delay disease progression. In addition, knee sleeve with patella cutout does not provide additional benefits when compared to the simple knee sleeve. © IMechE 2019.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.002 | 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".