Dynamically tensioned ACL functional knee braces reduce ACL and meniscal strain
Bibliographic record
Abstract
PURPOSE: The effectiveness of ACL functional knee braces to reduce meniscal and ACL strain after ACL injury or reconstruction is not well understood. A new dynamic knee tensioning brace system has been designed to apply an active stabilizing force to the knee. The ability of this system to reduce tissue strains is unknown. The purpose of this study was to test the ability of the dynamically tensioned brace to reduce strain in both the ACL and meniscus during rehabilitation activities. METHODS: A combined in vivo/in silico/in vitro method was used to study three activities: gait, double leg squat, and single leg squat. Muscle forces and kinematics for each activity were derived through in vivo motion capture and applied to seven cadaveric knee specimens fitted with custom braces. Medial meniscal strain and ACL strain were measured in ACL intact, deficient and reconstructed conditions. RESULTS: The brace lowered peak and average meniscal strain in ACL deficient knees (P < 0.05) by an average of 1.7%. The brace was also found to lower meniscal strain in reconstructed knees (1.1%) and lower ACL strain in ACL intact (1.3%) and reconstructed knees (1.4%) (P < 0.05). CONCLUSIONS: This study supports the use of a brace equipped with a dynamic tensioning system to lower meniscal strain in ACL-deficient knees. Its use may help decrease the risk of subsequent meniscal tears in chronic ACL deficiency or delayed reconstruction. In ACL-intact and reconstructed knees, the brace may be beneficial in injury prophylaxis or in protecting the ACL graft following reconstruction. These results will aid clinicians make informed recommendations for functional brace use in patients with unstable knees. LEVEL OF EVIDENCE: II.
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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) | 0.000 | 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".