Is radiofrequency ablation effective in treating patients with chronic knee osteoarthritis? A meta-analysis of randomized controlled trials
Bibliographic record
Abstract
Background and aims: This meta-analysis aimed to evaluate the short-term and long-term efficacy of radiofrequency ablation (RFA) and explore the role of diagnostic genicular nerve blocks in predicting treatment outcomes. Methods: A comprehensive literature search was conducted, and nine randomized controlled trials involving 714 participants were included in the analysis. Data extraction, risk of bias assessment, and subgroup analyses were performed. The primary outcome measures were pain scores at 6 and 12 months, assessed using visual analogue scale and Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC). The quality of evidence was assessed using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) methodology. Results: The meta-analysis revealed that RFA demonstrated a significant short-term efficacy in reducing pain compared to the control group at 6 months, as indicated by the pain scores [weighted mean difference (WMD): -2.69, 95% CI: -3.99, -1.40]. Similarly, WOMAC scores at 6 months favored the RFA group (WMD: -4.40, 95% CI: -7.12, -1.68). However, the long-term efficacy of RFA at 12 months remained uncertain for both pain scores (WMD: -0.88, 95% CI: -2.36, 0.61) and WOMAC (WMD: 0.03, 95% CI: -0.25, 0.32). Subgroup analysis suggested that a positive result from the diagnostic genicular nerve blocks test was associated with a more favourable short-term outcome. Conclusion: This meta-analysis provides moderate-quality evidence supporting the short-term efficacy of RFA in reducing pain in patients with knee osteoarthritis. The inclusion of a diagnostic genicular nerve blocks test prior to RFA may help identify patients likely to benefit from the procedure. But it still needs more large sample studies to verify the results. However, further research is needed to determine the long-term efficacy of RFA in managing knee osteoarthritis pain.
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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 | 0.028 | 0.056 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.061 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 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".