Pain and Functional Scores in Patients Affected by Knee OA after Treatment with Pulsed Electromagnetic and Magnetic Fields: A Meta-Analysis
Bibliographic record
Abstract
Objective The purpose of this systematic review and meta-analysis was to evaluate the effect of electromagnetic field treatment on the symptoms of knee osteoarthritis (OA). In addition, the influence of the type of control group and other covariates have been investigated to identify the sources of heterogeneity in the results of the available clinical trials. Methods Randomized controlled trials reporting pulsed electromagnetic field–based therapies for the treatment of knee OA have been included. Main outcomes were self-reported pain and activity scores collected by Visual Analogue Scale (VAS) and/or Western Ontario McMaster Universities Osteoarthritis Index (WOMAC) at short term after treatment. Results Thirteen studies comprising 914 unique patients were included in the analysis. Overall reduction in pain score was observed after treatment (standardized mean difference −0.4059, P = 0.0091), while improvement in the activity score was not significant (standardized mean difference −0.4452, P = 0.0859). Type of control (i.e., placebo or alternative therapies) and time of follow-up resulted as the two major elements influencing the outcomes. Indeed, the restriction of the analysis to placebo-controlled trials demonstrated higher standardized mean differences between treatment and control groups, with lower P value for pain, while statistical significance became evident also for the activity score. On the contrary, no differences were observed pooling only studies comparing pulsed electromagnetic or magnetic fields to alternative treatments. In addition, longer follow-up correlated with lower differences between treated and control patients. Conclusions Pulsed electromagnetic field therapy effectively relieves knee OA symptoms at short term, but it is not superior to other conservative therapies such as physiotherapy.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.021 | 0.046 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".