Baseline Gait Muscle Activation Patterns Differ for Osteoarthritis Patients Who Undergo Total Knee Arthroplasty Five to Eight Years Later From Those Who Do Not
Bibliographic record
Abstract
OBJECTIVE: To determine if baseline quadriceps and hamstrings muscle activity patterns differed between those with medial-compartment knee osteoarthritis (OA) who advanced to total knee arthroplasty (TKA) and those who did not advance to TKA, and to examine associations between features extracted from principal component analysis (PCA) and discrete measures. METHODS: Surface electromyograms of the vastus lateralis and medialis, rectus femoris, and lateral and medial hamstrings during walking were collected from 54 individuals with knee OA. Amplitude and temporal characteristics from PCA, co-contraction indices (CCI) for lateral and medial muscle pairs, and root mean square (RMS) amplitudes for early, mid, late, and overall stance were calculated from electromyographic waveforms. At follow-up 5 to 8 years later, 26 participants reported having undergone TKA. Analysis of variance models tested for differences in principal component (PC) scores and discrete measures between TKA and no-TKA groups (α = 0.05). Pearson's product moment correlation coefficients were calculated between PC scores and discrete variables. RESULTS: The TKA group had higher hamstrings activity magnitudes (PC1), prolonged activity in mid stance (PC2) for all muscles, and greater lateral CCI. TKA had higher RMS hamstrings activity for all stance phases, and higher RMS mid- and late-stance quadriceps activity. PC1 was highly correlated with RMS amplitude (highest overall and early stance). PC2 was correlated with mid- and late-stance RMS. CCIs were correlated with PC1 and PC2, with greater variance explained for PC1. CONCLUSION: Those who advanced to TKA had higher magnitudes and more prolonged agonist and antagonist activity, consistent with less joint unloading. These gait muscle activation patterns indicate a potential conservative intervention target.
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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 | 0.001 | 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".