The Effectiveness of Fluoroscopically Guided Genicular Nerve Radiofrequency Ablation for the Treatment of Chronic Knee Pain Due to Osteoarthritis
Bibliographic record
Abstract
ABSTRACT: The objective was to determine the effectiveness of fluoroscopically guided genicular nerve radiofrequency ablation for painful knee osteoarthritis. Primary outcome measure was improvement in pain after 6 mos. Secondary outcomes included the Oxford Knee Score and Western Ontario and McMaster Universities Osteoarthritis Index. Two reviewers independently assessed publications before October 10, 2020. The Cochrane Risk of Bias Tool and Grades of Recommendation, Assessment, Development, and Evaluation system were used. One hundred ninety-nine publications were screened, and nine were included. Six-month success rates for 50% or greater pain relief after radiofrequency ablation ranged from 49% to 74%. When compared with intra-articular steroid injection, the probability of success was 4.5 times higher for radiofrequency ablation (relative risk = 4.58 [95% confidence interval = 2.61-8.04]). When radiofrequency ablation was compared with hyaluronic acid injection, the probability of treatment success was 1.8 times higher (relative risk = 1.88, 95% confidence interval = 1.38-2.57). The group mean Oxford Knee Score and Western Ontario and McMaster Universities Osteoarthritis Index scores improved in participants receiving genicular radiofrequency ablation compared with intra-articular steroid injection and hyaluronic acid injection. According to Grades of Recommendation, Assessment, Development, and Evaluation, there is moderate-quality evidence that fluoroscopically guided genicular radiofrequency ablation is effective for reducing pain associated with knee osteoarthritis at minimum of 6 mos. Further research is likely to have an important impact on the current understanding of the long-term effectiveness of this treatment.
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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 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.011 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".