Knee Extensor Strength in Anterior Cruciate Ligament-Deficient Individuals Following Normal and Modified Squats: A Randomized Controlled Trial
Bibliographic record
Abstract
ABSTRACT: Jean, LMY, Gross, DP, and Chiu, LZF. Knee extensor strength in anterior cruciate ligament-deficient individuals following normal and modified squats: a randomized controlled trial. J Strength Cond Res 36(1): 47-54, 2022-Training with barbell squats, which load the quadriceps, increases knee extensor strength. Anterior cruciate ligament (ACL) injury results in a quadriceps avoidance substitution strategy, which may impair the efficacy of barbell squat training. Modified squats to promote quadriceps loading have been proposed to facilitate restoring knee extensor strength and function. This research compared resistance training using traditional squats (TRAD) versus traditional plus modified squats (EXP) in ACL-deficient individuals. Thirty-seven ACL-deficient individuals were randomly assigned to TRAD or EXP. Knee extensor function was assessed using maximum isometric strength testing and 3D motion analysis of sit-to-stand. Effect sizes for minimum meaningful improvement were established (d = 0.28-0.47). There were no significant differences between TRAD and EXP for knee extensor strength before, in response to, or after the training interventions (p > 0.05). Involved limb knee extensor strength increased at 15° (95% CI [0.09, 0.27] N·m·kgBM-1, d = 0.60), 30° (95% CI [0.25, 0.48] N·m·kgBM-1, d = 0.82), 45° (95% CI [0.32, 0.58] N·m·kgBM-1, d = 0.78), and 75° (95% CI [0.18, 0.46] N·m·kgBM-1, d = 0.54) knee flexion. Involved limb knee extensor net joint moment work in sit-to-stand (95% CI [0.034, 0.135] J·kgBM-1, d = 0.48) increased in both groups. Squat training was effective for increasing involved limb isometric knee extensor strength and knee extensor work performed in the sit-to-stand. Modified squats do not seem to provide additional benefit.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".