Alterations in lower limb multimuscle activation patterns during stair climbing in female total knee arthroplasty patients
Bibliographic record
Abstract
Total knee arthroplasty (TKA) patients commonly experience neuromuscular adaptations that may affect stair climbing competence. This study identified multimuscle pattern (MMP) changes in postoperative female TKA patients during stair climbing with a support vector machine (SVM). It was hypothesized that TKA patients adopt temporal and spectral muscle activation characteristics indicative of muscle atrophy and cocontraction strategies. Nineteen female subjects [10 unilateral sex-specific TKAs, 62.2 ± 8.6 yr, body mass index (BMI) 28.2 ± 5.4 kg/m(2); 9 healthy control subjects, 61.4 ± 7.4 yr, BMI 25.6 ± 2.4 kg/m(2)] were recruited. Surface electromyograms (EMGs) were obtained for seven lower limb muscles of the affected limb of TKA subjects and a randomly assigned limb for control subjects during stair climbing. Stance phase (±30%) EMG data were wavelet transformed and normalized to total power. Data across all muscles were combined to form MMPs and analyzed with a SVM. Statistical analysis was performed with binomial tests, independent group t-tests, or independent group Mann-Whitney U-tests in SPSS (P < 0.05). SVM results indicated significantly altered muscle activation patterns in the TKA group for biceps femoris (recognition rate 84.2%), semitendinosus (recognition rate 73.7%), gastrocnemius (recognition rate 68.4%), and tibialis anterior (recognition rate 68.4%). Further analysis identified no significant differences in spectral activation characteristics between groups. Temporal adaptations, indicative of cocontraction strategies, were, however, evident in TKA MMPs. This approach may provide a valuable tool for clinical neuromuscular function assessment and rehabilitation monitoring.
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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 | 0.000 |
| 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".