Biomechanical Deviations During Level Walking Associated With Knee Osteoarthritis: A Systematic Review and Meta‐Analysis
Bibliographic record
Abstract
OBJECTIVE: To identify which gait deviations are consistently associated with knee osteoarthritis (KOA) and how these are influenced by disease severity, the involved compartment, and sex. METHODS: Five electronic databases and reference lists of publications were searched. Cross-sectional, observational studies comparing temporospatial variables, joint kinematics, and joint moments between individuals with KOA and healthy controls or between KOA subgroups were considered for review. Only publications scoring ≥50% on a modified methodology quality index were included. Because of the number of gait deviations examined, only biomechanical variables reported by ≥4 publications were further analyzed. Where possible, a meta-analysis was performed using effect sizes (ES) calculated from discrete variables. RESULTS: In total, 41 publications examining 20 variables were included. The majority of consistent gait deviations associated with KOA were exhibited by those with severe disease in the temporospatial domain. Individuals with severe KOA exhibited greater stride duration than controls (ES 1.35 [95% confidence interval (95% CI) 1.03, 1.67]) and a decrease in cadence (ES -0.75 [95% CI -1.12, -0.39]) compared with controls. The evidence for kinematic and joint moment change was primarily limited or conflicting. There was a lack of evidence for alterations in the external knee adduction moment. CONCLUSION: Individuals with KOA exhibit a range of gait deviations compared with controls. Despite its common usage in KOA gait studies, we did not find consistent evidence that knee adduction moment differs between those with and without KOA or between disease severity levels. Further research examining the reasons for a lack of difference in many gait variables in those with knee OA is needed.
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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 | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.022 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".