Knee biomechanics during walking in individuals with anterior cruciate ligament repair: The role of a custom 3D-printed knee brace
Bibliographic record
Abstract
BACKGROUND: Anterior cruciate ligament (ACL) injuries frequently lead to altered gait biomechanics and muscle activation patterns, increasing the risk of osteoarthritis. Knee braces are commonly used to address these issues although a lack of consensus remains regarding their clinical benefits. The most recent 3D-printed braces, lighter and personalized, could improve rehabilitation. OBJECTIVES: To evaluate the effect of a novel custom-made 3D-printed knee brace (Provoke™) in individuals after unilateral ACL reconstruction during walking. The brace incorporates an asymmetrical hinge system aimed at stabilizing the knee joint while minimizing compensatory movements. METHODS: Fourteen participants with unilateral ACL reconstruction wore the Provoke™ brace while walking at comfortable and fast paces. Knee kinematics and kinetics and muscular activity (rectus femoris, vastus medialis, and semitendinosus) were assessed with and without the brace. Nonparametric paired t tests were used to assess the biomechanical effect of the brace. RESULTS AND CONCLUSIONS: The Provoke™ brace improved knee kinematics, facilitating a more neutral knee position by reducing valgus angles (-1.95°) and increasing flexion angles (+1.14°). In addition, it enhanced muscle activation, particularly of the rectus femoris, suggesting improved quadriceps function. Overall, the Provoke™ brace effectively improves knee function and reduces muscle imbalances in individuals undergoing ACL reconstruction. It may help prevent further injury and reduce the risk of post-traumatic osteoarthritis development. The long-term effects of brace use in ACL rehabilitation must be investigated.
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) | 0.002 | 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".