Efficacy and safety of radiofrequency treatment for improving knee pain and function in knee osteoarthritis: a meta-analysis of randomized controlled trials
Bibliographic record
Abstract
BACKGROUND: The clinical utility of radiofrequency (RF) in patients with knee osteoarthritis (OA) remains unclear. We conducted a meta-analysis to systematically evaluate the efficacy and safety of RF treatment in patients with knee OA. METHODS: Searches of the PubMed, Web of Science, EMBASE, Cochrane Library, China National Knowledge Infrastructure, and Wanfang Data databases were performed through August 30, 2021. The major outcomes from published randomized controlled trials (RCTs) involving patients with knee OA were compared between RF and control groups, including Visual Analogue Scale (VAS) or Numerical Rating Scale (NRS) scores, the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), Oxford Knee Score (OKS), Global Perceived Effect (GPE) scale, and adverse effects at available follow-up times. RESULTS: Fifteen RCTs involving 1009 patients were included in this meta-analysis, and the results demonstrated that RF treatment correlated with improvements in pain relief (VAS/NRS score, all P < 0.001) and knee function (WOMAC, all P < 0.001) at 1-2, 4, 12, and 24 weeks after treatment as well as patients' degree of satisfaction with treatment effectiveness (GPE scale, 12 weeks, P < 0.001). OKSs did not differ significantly between the two groups. Moreover, treatment with RF did not significantly increase adverse effects. Subgroup analysis of knee pain indicated that the efficacy of RF treatment targeting the genicular nerve was significantly better than intra-articular RF at 12 weeks after treatment (P = 0.03). CONCLUSIONS: This meta-analysis showed that RF is an efficacious and safe treatment for relieving knee pain and improving knee function 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 | 0.023 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.049 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 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".