The Effect of Functional Knee Braces on Muscular Contributions to Joint Rotational Stiffness in Anterior Cruciate Ligament–Deficient and –Reconstructed Patients
Bibliographic record
Abstract
Functional knee braces are frequently prescribed by physicians to ameliorate the function of individuals with anterior cruciate ligament (ACL) injuries. These braces have been shown in the literature to potentially enhance knee stability by augmenting muscle activation patterns and the timing of muscle response to perturbations. However, very few techniques are available in the literature to quantify how those modifications in lower-limb muscle activity influence stability of the knee. The aim of the present study was to quantify the effect of an off-the-shelf functional knee brace on muscle contributions to knee joint rotational stiffness in ACL-deficient and ACL-reconstructed patients. Kinematic, electromyography, and kinetic data were incorporated into an electromyography-driven model of the lower extremity to calculate individual and total muscle contributions to knee joint rotational stiffness about the flexion-extension axis, for 4 independent variables: leg condition (contralateral uninjured, unbraced ACL injured, and braced ACL injured); knee flexion (5°-10°, 20°-25°, and 30°-35°); squat stability condition (stable and unstable); and injury status (ACL deficient and ACL reconstructed). Participants had significantly higher (P < .05, η2 = .018) total knee joint rotational stiffness values while wearing the brace compared with the control leg. A 2-way interaction effect between stability and knee flexion (P < .05, η2 = .040) for total joint rotational stiffness was also found.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".