Magnet Therapy versus Electroacupuncture for Knee Osteoarthritis: A Double-Blind Randomized Clinical Trial
Bibliographic record
Abstract
Background: Knee osteoarthritis (KOA) is a prevalent degenerative joint disorder often managed with pharmacological therapies, which may cause adverse effects. This study aimed to evaluate and compare the effects of magnet therapy (MT) and electroacupuncture (EA) in the treatment of KOA. Methods: A randomized, double-blind trial involved 93 KOA patients divided into three groups: MT, EA, and routine treatment (RT). Eligible participants had confirmed KOA, pain lasting over three months, and walking ability. Outcomes, assessed by a specialist, included pain intensity, 6-minute walk test (6MWT), and knee function, stiffness, and pain via the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) at baseline, after 10 treatment sessions, and one-month post-treatment. The trial was registered with the Iranian Registry of Clinical Trials (IRCT20100129003220N11). Results: Participants had a mean age of 67.44±5.18 years. Significant differences were observed across groups in pain, 6-minute walk test, WOMAC scores, stiffness, and physical function (P<0.001). All variables showed significant changes over the three time points (P<0.05), with distinct trends in mean changes among groups (P<0.001). Conclusion: MT outperformed EA and RT in reducing pain, stiffness, and WOMAC scores while improving physical function and 6MWT outcomes in KOA 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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".