Elevating the Noninvolved Limb Reduces Knee Extensor Asymmetry During Squat Exercise in Persons With Reconstructed Anterior Cruciate Ligament
Bibliographic record
Abstract
Jean, LMY and Chiu, LZF. Elevating the noninvolved limb reduces knee extensor asymmetry during squat exercise in persons with reconstructed anterior cruciate ligament. J Strength Cond Res 34(8): 2120-2127, 2020-Anterior cruciate ligament (ACL)-injured individuals use less knee extensor net joint moment (NJM) in the involved vs. noninvolved limb during squat exercises. The objective of this research was to examine if knee extensor NJM symmetry between the involved and noninvolved limbs could be attained with a modified squat. Six individuals with unilateral ACL reconstructed knees performed bilateral squats under normal conditions and with their noninvolved limb elevated on a 5-cm platform. Knee extensor NJM was determined using 3-dimensional motion analysis. Knee extensor NJM was lower in the involved compared with the noninvolved limb (95% confidence interval [CI], 0.08-0.28 N·m·kg; d = 1.66 SD) during normal squats. Knee extensor NJM was greater in the involved (95% CI, 0.02-0.18 N·m·kg; d = 0.57 SD) and lower in the noninvolved (95% CI, -0.25 to -0.07 N·m·kg; d = 1.85 SD) limbs in the elevated vs. normal squats. Knee extensor NJM was not different between limbs when the noninvolved limb was elevated (95% CI, -0.26 to 0.11 N·m·kg; d = 0.48 SD). Persons with ACL reconstruction exhibit knee extensor asymmetry during bilateral squats. Elevating the noninvolved limb reduces knee extensor NJM asymmetry between the involved and noninvolved limbs during squat exercise.
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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) | 0.001 | 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".