Inter-joint coordination and lower limb support in those with ACL-reconstruction
Bibliographic record
Abstract
Individuals with anterior cruciate ligament reconstruction (ACLR) adopt altered walking patterns that shift support demands away from the surgical knee which may necessitate compensatory ankle or hip action to provide sufficient support. It is unclear how those with ACLR adapt and coordinate inter-joint motions to redistribute support demands during walking. Here, we compared lower-limb support and inter-joint coordination during walking in those with ACLR. Treadmill walking was evaluated in 28 individuals with ACLR and 20 healthy controls at preferred speed. The sum of sagittal joint moments in ankle, knee and hip was used to calculate total support moment (TSM) and individual joint contributions (%) to the TSM. Inter-joint coordination of ankle-knee and knee-hip was evaluated using a modified vector coding technique during early, mid and late stance. Paired t-tests compared TSM and joint contributions between-limbs (α = 0.05). Wilcoxon signed-rank tests compared coordination patterns (α = 0.05). We observed smaller 1st peak TSM in the ACLR limb (p < 0.01) and 6 % greater hip contributions in ACLR limbs (p = 0.02). We observed greater ankle motions in early and midstance, and greater hip motions in mid-late stance in ACLR limbs relative to comparison limbs. Overall, the ACLR limb exhibited coordination alterations characterized by increased reliance on ankle and knee motions to accommodate rigid knee mechanics throughout stance compared to non-ACLR and control limbs. Together, these joint coordination strategies may reduce and/or redistribute support demands in the ACLR limb to lessen muscular requirements for support and propulsion.
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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) | 0.002 | 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".