Quantifying Knee Strength Deficits and Muscular Contribution to Torque Generation in ACL-Injured Adolescents Using A Simple Musculoskeletal Model
Bibliographic record
Abstract
Background: Surface electromyography (sEMG) is commonly used to elucidate the effect of anterior cruciate ligament (ACL) injury on neuromuscular function. To facilitate comparisons, sEMG is normalized to a known value, such as peak activation during a maximum voluntary isometric contraction (MVIC). However, voluntary muscle inhibition after an injury compromises one’s ability to achieve a true MVIC value. Musculoskeletal modeling can be used to quantify this strength deficit by comparing a theoretical MVIC torque to the actual MVIC torque. Purpose: This study sought to evaluate voluntary knee extensor and flexor strength deficits and individual muscle contribution to peak torque generation in ACL injured (ACLi) and uninjured (CON) adolescent populations using a simplified subject-specific modeling framework. Methods: Thirty-nine ACLi (25 females) and 39 CON (25 females) adolescents (12-17years) completed knee extension and flexion MVICs on an isokinetic dynamometer. Peak experimental torque (TE) was identified. A subject-specific modeling framework used normalized sEMG of the quadriceps, hamstrings, and gastrocnemius muscles to determine theoretically ideal torque (TI) for each exercise, assuming agonist muscles were fully activated. Strength deficit ratios (TE/TI) and individual muscle contribution to TE were computed. Group mean differences were compared using independent t-tests. Results: ACLi demonstrated significantly lower extension (2.53±0.94 vs 3.07±0.57Nm/kg, p<0.001) and flexion (1.14±0.50 vs 1.37±0.31Nm/kg, p<0.001) peak TE compared to CON (FIG 1.A). Significant between group differences in sEMG of antagonist muscles were observed. Both groups demonstrated similar TI values (FIG 1.B-upper). Significantly lower trends in strength ratios (TE/TI) were maintained between groups (FIG 1.B-lower). However, percent between group differences were minimized from 17.8% to 6.3% for knee extension, and 16.7% to 10.0% for knee flexion, when TE is expressed relative to TI. Significantly lower medical gastrocnemius contribution to flexion TE was also observed in the ACLi (24.7±0.08%) compared to CON (30.9±10.1%, p=0.037). Conclusion: Significant between group differences in peak extensor or flexor torques were observed. Our model confirmed a strength deficit in ACLi compared to CON, however, between group differences were less prominent when described relative to TI. This may be attributed to between group differences in antagonist contributions (i.e. co-contraction) during MVIC exercise. Simplified modeling frameworks that can incorporate muscle activations as well as torque generation may be more appropriate for evaluating functional outcomes of ACLi populations in a clinical setting. [Figure: see text]
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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 | 0.001 | 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".