Associations Between Cadence and Knee Loading in Patients With Knee Osteoarthritis
Bibliographic record
Abstract
OBJECTIVE: To test the hypothesis that higher walking cadence is associated with lower knee loading, while controlling for walking speed, in patients with medial compartment tibiofemoral osteoarthritis (OA). METHODS: A total of 691 patients underwent quantitative gait analysis, including 3-dimensional knee moments and temporospatial parameters. Using multivariate linear regression, we tested the association of walking cadence with the knee adduction moment angular impulse (a surrogate measure of medial knee compartment load throughout the stance), while controlling for walking speed. We repeated the analysis while also adjusting for sex, age, body mass index, radiographic OA, knee pain, lateral trunk lean, foot progression angle, and mechanical axis angle, and while replacing the knee adduction moment angular impulse with other surrogate measures of knee loading. RESULTS: While controlling for walking speed, we found that a lower cadence was associated with higher knee adduction moment angular impulse (standardized β = -0.396, P < 0.001), suggesting a 0.02% body weight × height × seconds (%BW × Ht × s) decrease in impulse for each step per minute increase in cadence (unstandardized β -0.020 %BW × Ht × s [95% confidence interval -0.027, -0.015]), and remained consistent after adjusting for covariates. A lower cadence was also associated with higher first (standardized β = -0.138, P = 0.010) and second peak knee adduction moment (standardized β = -0.132, P = 0.018), higher peak knee flexion moment (standardized β = -0.128, P = 0.049), and vertical ground reaction force (standardized β = -0.116, P = 0.035) in the adjusted analyses. CONCLUSION: When controlling for walking speed, we found that a lower cadence is associated with higher knee loading per step in patients with medial tibiofemoral OA. Future research should investigate the potential beneficial biomechanical and clinical effects of increasing walking cadence in patients with knee OA.
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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 | 0.003 |
| 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 | 0.001 | 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".