The effect of unloader knee braces on medial meniscal strain
Bibliographic record
Abstract
BACKGROUND:: A medial meniscal tear is a common knee injury, especially following an anterior cruciate ligament injury. Decreasing the compressive force on the medial meniscus during dynamic activities using an unloader knee brace could reduce meniscal strain, effectively reducing injury risk and/or severity. OBJECTIVES:: To investigate the efficacy of two unloader knee braces on medial meniscus strain during dynamic activities in intact & deficient anterior cruciate ligament states. STUDY DESIGN:: Combined in vivo/in vitro study. METHODS:: In vivo knee kinematics and muscle force profiles from a healthy individual performing single/doubleleg squats and walking motions were simulated on 10 cadaveric specimens using a dynamic knee simulator system. Simulations were performed on knees in unbraced and braced scenarios, with and without the anterior cruciate ligament. Anterior and posterior medial meniscal strains were measured. RESULTS:: Two different braces each showed a significant reduction in the posteromedial meniscal strain ( p ⩽ 0.01) in an intact anterior cruciate ligament state. Neither brace mirrored this result for the anteromedial strain ( p > 0.05). In the deficient anterior cruciate ligament state, the braces had no significant effect on strain ( p > 0.05). CONCLUSION:: Two unloader knee braces effectively reduced strain in the medial meniscus with an intact anterior cruciate ligament during dynamic activities. Neither brace made a significant reduction in strain for anterior cruciate ligament-deficient knees. CLINICAL RELEVANCE: Unloader knee braces could be used to reduce the medial meniscus strain following meniscal surgery and during rehabilitation in patients with an isolated medial meniscus injury. However, these braces cannot be recommended for this purpose in patients with an anterior cruciate ligament deficiency.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 | 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".