Neuromuscular electrical stimulation (NMES) reduces structural and functional losses of quadriceps muscle and improves health status in patients with knee osteoarthritis
Bibliographic record
Abstract
Knee osteoarthritis (OA) is associated with quadriceps atrophy and weakness, so muscle strengthening is an important point in the rehabilitation process. Since pain and joint stiffness make it often difficult to use conventional strength exercises, neuromuscular electrical stimulation (NMES) may be an alternative approach for these patients. This study was aimed at (1) identifying the associations of knee OA with quadriceps muscle architecture and strength, and (2) quantifying the effects of a NMES training program on these parameters. In phase 1, 20 women with knee OA were compared with 10 healthy female, asymptomatic, age-matched control subjects. In phase 2, 12 OA patients performed an 8-week NMES strength training program. OA patients presented smaller vastus lateralis thickness (11.9 mm) and fascicle length (20.5%) than healthy subjects (14.1 mm; 24.5%), and also had a 23% smaller knee extensor torque compared to the control group. NMES training increased vastus lateralis thickness (from 12.6 to 14.2 mm) and fascicle length (from 19.6% to 24.6%). Additionally, NMES training increased the knee extensor torque by 8% and reduced joint pain, stiffness, and functional limitation. In conclusion, OA patients have decreased strength, muscle thickness, and fascicle length in the knee extensor musculature compared to control subjects. NMES training appears to offset the changes in quadriceps structure and function, as well as improve the health status in patients with 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 | 0.000 | 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".