Joint and Limb Loading during Gait in Adults with ACL Reconstruction: Comparison between Single-Step and Cumulative Load Metrics
Bibliographic record
Abstract
PURPOSE: Individuals with anterior cruciate ligament reconstruction (ACLR) generally exhibit limb underloading behaviors during walking, but most research focuses on per-step comparisons. Cumulative loading metrics offer unique insight into joint loading as magnitude, duration, and total steps are considered, but few studies have evaluated if cumulative loads are altered post-ACLR. Here, we evaluated if underloading behaviors are apparent in ACLR limbs when using cumulative load metrics and how load metrics change in response to walking speed modifications. METHODS: Treadmill walking biomechanics were evaluated in 21 participants with ACLR at three speeds (self-selected (SS); 120% SS and 80% SS). Cumulative loads per step and per kilometer were calculated using knee flexion and adduction moment (KFM and KAM) and vertical ground reaction force (GRF) impulses. Traditional magnitude metrics for KFM, KAM, and GRF were also calculated. RESULTS: The ACLR limb displayed smaller KFM and GRF in early and late stances, but larger KFM and GRF during midstance compared with the contralateral limb ( P < 0.01). Only GRF cumulative loads (per step and per kilometer) were reduced in the ACLR limb ( P < 0.01). In response to speed modifications, load magnitudes generally increased with speed. Conversely, cumulative load metrics (per step and per kilometer) decreased at faster speeds and increased at slow speeds ( P < 0.01). CONCLUSIONS: Patients with ACLR underload their knee in the sagittal plane per step, but cumulatively over the course of many steps/distance, this underloading phenomenon was not apparent. Furthermore, cumulative load increased at slower speeds, opposite to what is identified with traditional single-step metrics. Assessing cumulative load metrics may offer additional insight into how load outcomes may be impacted in injured populations or in response to gait modifications.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".