Differences in EMG–moment relationships between ACL‐injured and uninjured adults during a weight‐bearing multidirectional force control task
Bibliographic record
Abstract
Anterior cruciate ligament injury (ACLi) reduces mechanical knee joint stability. Differences in muscle activation patterns are commonly identified between ACLi individuals and uninjured controls (CON); however, how and which of these differences are adaptations to protect the knee or adversely increase risk of joint instability remain unclear. Since the neuromuscular system integrates activity of all muscles crossing the knee to create a moment-of-force that opposes an external load, this study sought to quantify differences in individual muscle electromyography (EMG)-moment relationships between ACLi and CON. Participants isometrically modulated ground reaction forces during a standing force matching protocol to elicit combinations of sagittal, frontal and transverse plane moments. Partial least squares regressions determined which internal joint moment(s) predicted activation of 10 leg muscles for each group. Compared to CON, ACLi demonstrated greater contribution of rectus femoris to knee extension, semitendinosus and gastrocnemii to knee flexion, and lateral gastrocnemii to knee external rotation moments. ACLi also showed lower contributions of biceps femoris to knee flexion, medial gastrocnemius to internal rotation, and varied hip muscle contributions to frontal plane hip moments. Between group differences in EMG-moment relationships during static conditions suggest neuromuscular contributions to sagittal plane stability increases after ACL injury, while knee stability during knee abduction and external rotation is reduced. Clinical Significance: Clinical assessments of ACLi should account for deficits in frontal and rotational plane stability by including tasks that elicit such loads. Improving hamstring muscle balance, hip abductor and gastrocnemius function may benefit ACLi rehabilitation interventions and should be studied further. © 2018 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res.
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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".