Efficacy and Safety of Nerve Ablation Techniques Versus Sham Procedures in Knee Osteoarthritis: A Systematic Review and Meta‐Analysis of Randomized Clinical Trials
Bibliographic record
Abstract
BACKGROUND: Ablative techniques, such as radiofrequency (RF) and cryoneurolysis (CN), are emerging as minimally invasive options for knee osteoarthritis (OA) pain management. However, inconsistencies in efficacy, technique variations, and a lack of standardized protocols limit their clinical application. This study addresses these gaps through a comprehensive meta-analysis. OBJECTIVES: This systematic review and meta-analysis aimed to evaluate the efficacy of ablative techniques in improving (1) pain, measured by the Visual Analog Scale (VAS), (2) functionality, assessed using the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), and (3) quality of life and patient perception/satisfaction at 4, 12, and 24 weeks after treatment in patients with OA. METHODS: A meta-analysis of randomized controlled trials (RCTs) was conducted using PubMed, Embase, Scopus, and the Cochrane Database up to October 2024. Studies were included evaluating genicular or saphenous nerve ablation in adults with OA-associated pain. Outcomes of interest comprised pain reduction (VAS), functional improvement (WOMAC), patient perception, and quality of life. The risk of bias was evaluated using the RoB 2 tool, and data were analyzed using fixed- or random-effects models based on the degree of heterogeneity. RESULTS: Seven studies involving a total of 437 patients were included. The meta-analysis demonstrated a significant reduction in pain in the ablation group at 4 weeks (MD = -1.60; 95% CI = -2.79 to -0.41), 12 weeks (MD = -1.97; 95% CI = -2.54 to -0.80), and 24 weeks (MD = -0.82; 95% CI = -1.41 to -0.23). CN consistently reduced pain (MD = -2.25 at 4 weeks, -1.12 at 12 weeks, and -1.28 at 24 weeks). Additionally, most evaluations did not show significant differences in patient perception and quality of life measures. CONCLUSION: Ablative techniques, particularly CN, show promise in reducing pain and enhancing function in patients with OA, offering a minimally invasive alternative for those with an inadequate response to conservative treatments. However, the observed outcome variability underscores the need for further research to standardize protocols and confirm long-term safety and efficacy.
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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 | 0.021 | 0.045 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.043 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 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".