Can a knee brace reduce the strain in the anterior cruciate ligament? A study using combined in vivo/in vitro method
Bibliographic record
Abstract
BACKGROUND AND AIM: It is unknown whether prophylactic knee braces can reduce the strain in the anterior cruciate ligament during dynamic activities. TECHNIQUE: An athlete, who had characteristics of high anterior cruciate ligament injury risk, was chosen. A motion capture system (Optotrak Certus; Northern Digital, Waterloo, ON, Canada) was used to record dynamic trials during drop-landing activity of this subject with and without the knee brace being worn. A musculoskeletal model was used to estimate the muscle forces during this activity. A dynamic knee simulator then applied kinematics and muscle forces on a cadaver knee with and without the brace mounted on it. The anterior cruciate ligament strain was measured. DISCUSSION: The peak strain in the anterior cruciate ligament was substantially lower for the braced (7%) versus unbraced (20%) conditions. Functional knee braces could decrease the strain in the anterior cruciate ligament during dynamic activities in a high-risk subject. However, the reduction seems to be a result of altered muscle firing pattern due to the brace. CLINICAL RELEVANCE: Prophylactic knee brace could reduce the strain in the anterior cruciate ligament of high-risk subjects during drop-landing through altered muscle firing pattern associated with brace wear. This could help reduce the anterior cruciate ligament injury risk.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".