The effectiveness of massage on pain, external knee adduction moment, and muscle Co-contraction in individuals with medial compartment knee osteoarthritis
Bibliographic record
Abstract
BACKGROUND: The pain, external knee adduction moment (EKAM), and muscle co-contraction are increased in knee osteoarthritis (KOA). Massage therapy decreases pain in KOA, yet KOA is a mechanical disease and biomechanical changes need to be investigated as well. Therefore, the current study aims to investigate the effectiveness of massage on these outcomes in individuals with medial KOA. METHODS: ) was given a six-week massage. Outcomes assessed pre- and post-intervention were: Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) scores, temporal-spatial variables, knee joint kinematics and kinetics in sagittal, frontal, and transverse planes, vertical ground reaction force (GRF), and knee antagonist muscle co-contraction during gait. The paired t-test were used for statistical analysis. RESULTS: Fifteen participants completed the study. Significant improvements were observed in WOMAC scores (pain, stiffness, function, and total), walking speed, step length, 1st peak GRF, sagittal plane knee joint range of motion during stance, and medial muscle co-contraction in early and mid-stance (p < 0.05). However, no significant change was found in EKAM and knee adduction angular impulse (KAAI) (p > 0.05). CONCLUSION: Massage therapy, as a stand-alone treatment, reduces pain, improves function, and decreases medial muscle co-contraction in individuals with medial KOA. Although EKAM did not change, the results suggest a reduction in medial muscle co-contraction might be a mechanism by which pain is improved.
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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".