Comparison of Ultrasound-Guided Genicular Pulse Radiofrequency and Fluoroscopy-Guided Intra-Articular Pulse Radiofrequency for Knee Osteoarthritis-Related Pain
Bibliographic record
Abstract
OBJECTIVE: To compare two different algological intervention technique outcomes with ultrasound-guided genicular pulse radiofrequency (PRF) and fluoroscopy-guided intra-articular pulse radiofrequency for knee osteoarthritis-related pain. STUDY DESIGN: Observational study. Place and Duration of the Study: Izmir Bakircay University, Cigli Training and Research Hospital and Health Science University Tepecik, Training and Research Hospital, Izmir, Turkiye, between March 2022 and May 2023. METHODOLOGY: Patients aged 60 years and above with stage 3 and 4 knee osteoarthritis, experiencing knee pain for more than six months, and non-responsive to conservative treatments were included. Patients with recent knee surgery or intra-articular injections and those ineligible for radiofrequency application were excluded. Ultrasound-guided genicular nerve PRF and fluoroscopy-guided intra-articular PRF were administered to the included patients. Pain and quality of life were evaluated using the visual analogue scale (VAS) and Western Ontario and McMaster Universities Index of Osteoarthritis (WOMAC) scores before and after the procedures. RESULTS: The study included 64 patients. Both ultrasound-guided genicular PRF and fluoroscopy-guided intra-articular PRF resulted in significant reductions in VAS and WOMAC scores at 1 and 3 months after the procedures. There was no significant difference in efficacy between the two techniques. CONCLUSION: Ultrasound-guided genicular PRF and fluoroscopy-guided intra-articular PRF are effective and safe options for managing knee osteoarthritis-related pain. KEY WORDS: Osteoarthritis, Pulse radiofrequency, Ultrasound, Fluoroscopy, 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.001 | 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".