Effectiveness of Neuromuscular exercises (NEMEX) in knee osteoarthritis: A Systematic Review with meta-analysis
Bibliographic record
Abstract
Abstract The main objective of the article was to evaluate the effectiveness of Neuromuscular exercises (NEMEX) on pain and function in patients with knee osteoarthritis. Pubmed, Cochrane, PEDro and Google Scholar were searched. The eligibility criteria were: Randomized controlled trials, single blinded controlled trials, controlled before and after study comparing and assessing the effectiveness of NEMEX in knee osteoarthritis, articles published in English language till 2020 with NEMEX either alone or in conjunction with other interventions (drugs, educational packages) in patients with knee osteoarthritis. Outcome measures used for meta-analysis were visual analog scale (VAS) for pain and Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) for function and the other measures Knee Injury Osteoarthritis Outcome Score (KOOS), Knee Index and Knee Adduction Moment (KAM) and Hip Disability and Osteoarthritis Outcome Score (HOOS). The result of the meta-analysis for the included two studies showed statistically significant reduction in pain (VAS) in the NEMEX group as compared to the other group Z = 0.64, p = 0.03, I2 = 79% and no significant reduction in WOMAC scores Z = 0.70, P = 0.64. The study concludes that there was statistically significant improvement in pain (VAS) but no functional improvement (WOMAC) with the NEMEX whether used in isolation or in conjunction with other interventions was seen in the patients with knee OA. However, large numbers of studies are required to generalize the effectiveness of NEMEX in knee osteoarthritis. Based on this review, NEMEX can be used as a potential intervention in reducing pain, improving strength and function in knee osteoarthritis patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.040 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".