Effectiveness of Radiofrequency Ablation of Genicular Nerves in the Treatment of Chronic Knee Pain due to Degenerative Osteoarthritis: A Prospective Interventional Study
Bibliographic record
Abstract
Introduction: Radiofrequency Ablation (RFA) has a long history dating back to 1931, with applications in treating various pain conditions. Given its established efficacy in managing spinal facet joint pain and its emerging popularity in treating arthritic knee pain through Genicular Nerve RFA (GNRFA), it is essential to investigate the effectiveness of traditional RFA in alleviating knee pain due to degenerative Osteoarthritis (OA) in patients with advanced OA who are not candidates for Total Knee Arthroplasty (TKA). Aim: To evaluate the effectiveness of RFA of genicular nerves in reducing chronic knee pain due to degenerative OA. Materials and Methods: This prospective interventional study was conducted at the Department of Orthopaedic Surgery, JSS Medical College, Mysore, Karnataka, India over a period of one year from July 2022 to June 2023. A total of 38 patients with chronic knee pain resulting from degenerative OA underwent RFA of genicular nerves, with assessments conducted at four time points: baseline, one month, three months and six months postprocedure. The evaluations utilised the Numerical Rating Scale (NRS) to measure pain levels, the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) to evaluate functional outcomes, and monitored analgesic usage to track pain management, providing a comprehensive understanding of the treatment’s effectiveness in alleviating chronic knee pain. The Friedman test was used to study the effectiveness of RFA of genicular nerves in reducing knee pain due to degenerative OA, and the pairwise Wilcoxon test with Bonferroni correction was used to compare statistical significance between different pairs. A p-value of <0.05 was considered statistically significant. Results: Substantial reductions were noted in pain levels, both at rest and during movement, as evidenced by significant reductions (p-value <0.001) in NRS scores, alongside improvements in WOMAC scores and decreased analgesic usage at all follow-up assessments compared to baseline (p-value <0.05). Conclusion: The study found that GNRFA significantly reduces pain and improves function in patients with chronic knee OA. This minimally invasive procedure is a safe, effective and costefficient treatment option for OA patients who rely heavily on analgesics, are unsuitable for surgery, or experience persistent pain after Total Knee Replacement (TKR).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".