Does pain relate with activation of quadriceps and hamstrings muscles during strengthening exercise in people with knee osteoarthritis?
Bibliographic record
Abstract
Muscle strengthening may be difficult to achieve in knee osteoarthritis (OA) due to pain. A large knee adduction moment (KAM), representing medial relative to lateral knee load, may also relate with pain during strengthening exercise. The objective of this study was to examine relationships between knee pain status and electromyography (EMG) amplitude of knee muscles during squat and lunge exercises. We also evaluated relationships between pain and KAM during these exercises. Forty-two women with symptomatic knee OA participated. Knee pain intensity and frequency were captured with two reliable and valid questionnaires. Motion analyses of squat and lunge exercises were completed. Total average EMG amplitude across five muscles of the lower limb and average KAM were calculated from the static portion of these exercises. Multiple regression analyses examined the relationships between pain and total average EMG amplitude; and pain and average KAM during squats and lunges. Pain improved the model for KAM from the trailing leg of a lunge. Pain did not improve any other model. Overall, pain may not be a useful indicator of EMG amplitude or KAM during exercise in 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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".