Effect of obesity on knee joint biomechanics during gait in young adults
Bibliographic record
Abstract
While there are many comorbidities associated with obesity, one of the more poorly understood is knee osteoarthritis through obesity. The purpose of this study was to compare the kinematics and kinetics of gait and cumulative knee adductor load, which represents the sum of repetitive exposures to medial knee loading during daily activity, between young obese adults with young, healthy-weight adults. Eight obese and eight healthy-weight young adults participated. Data from a three-dimensional motion capture system and a synchronized floor-mounted force plate were collected during gait trials. Participants wore accelerometers to determine step counts for seven consecutive days. Dependent t-tests were used to identify differences in gait kinematics, kinetics and cumulative knee adductor load between groups. Compared to the healthy-weight participants, obese young adults demonstrated a slower walking speed, greater stance duration, less knee flexion at heel contact, greater knee adduction in early stance and less knee abduction at terminal stance (p < 0.05). The obese young adults had a greater external knee extension moment (p < 0.05) and external rotation moment (p < 0.05) in early stance. The obese group had a greater cumulative knee adductor load. These results provide insight into a potential pathway by which obesity predisposes a healthy young adult for knee osteoarthritis.
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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".