Influence of body mass index and anterior cruciate ligament reconstruction on gait biomechanics
Bibliographic record
Abstract
Body mass index (BMI) and history of anterior cruciate ligament reconstruction (ACLR) independently influence gait biomechanics and knee osteoarthritis risk, but the interaction between these factors is unclear. The purpose of this study was to compare gait biomechanics between individuals with and without ACLR, and with and without overweight/obesity. We examined 104 individuals divided into four groups: with and without ACLR, and with low or high BMI (n = 26 per group). Three-dimensional gait biomechanics were evaluated at preferred speed. The peak vertical ground reaction force, knee flexion angle and excursion, external knee flexion moment, and external knee adduction moment were extracted for analysis. Gait features were compared between groups using 2 (with and without overweight/obesity) × 2 (with and without ACLR) analysis of variance. Primary findings indicated that those with ACLR and high BMI had a larger external knee adduction moment compared with those with low BMI and with (p = 0.004) and without ACLR (p = 0.005), and compared with those without ACLR and high BMI (p = 0.001). The main effects of ACLR and BMI group were found for the knee flexion moment, and those with ACLR and with high BMI had lower knee flexion moments compared with those without ACLR (p = 0.031) and with low BMI (p = 0.021), respectively. Data suggest that individuals with ACLR and high BMI may benefit from additional intervention targeting the knee adduction moment. Moreover, lower external knee flexion moments in those with high BMI and ACLR were consistent, but high BMI did not exacerbate deficits in the knee flexion moment in those with ACLR. [Correction added on 9 November 2022, after first online publication: In the preceding sentence, for clarity, the words "reductions in the lower" was removed from the initial sentence to read "Moreover, lower external knee flexion moments".].
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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 | 0.001 |
| 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".