Ultrasonography- and Fluoroscopy-Guided Technique for Cooled Radiofrequency Ablation of the Genicular Nerves for Knee Joint Pain
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Bibliographic record
Abstract
<h3></h3> <b>Background:</b> Knee osteoarthritis is a chronic degenerative disease associated with pain and decreased mobility that affects advanced-age individuals, thus causing further debilitation. Radiofrequency ablation can benefit patients who are not ideal candidates for surgical intervention and for whom conservative management has been unsatisfactory. Currently, radiofrequency ablation is performed using either ultrasonography or fluoroscopy. In this technique review, we propose a method of performing cooled radiofrequency ablation of the genicular nerves that uses both ultrasonography and fluoroscopy and that could be helpful for novice pain practitioners. <b>Case Series:</b> We report the experience of 2 patients with grade 4 osteoarthritis knee joints who underwent our cooled radiofrequency ablation technique. Each patient received a diagnostic block using ultrasonography, with ≥70% pain relief the prerequisite for performing cooled radiofrequency ablation. Our radiofrequency ablation technique involves using ultrasonography to identify and mark the superomedial, superolateral, and inferomedial genicular arteries. The marking done with ultrasonography is used during needle insertion with fluoroscopy guidance to reach the target points, and the final position of the needle is confirmed using sensory and motor stimulation before the cooled radiofrequency ablation procedure is performed. The cooled radiofrequency ablation resulted in pain reduction as measured on the visual analog scale and the Western Ontario and McMaster Universities Osteoarthritis Index scores at both patients’ 3- and 6-month follow-ups. <b>Conclusion:</b> Using this technique for cooled radiofrequency ablation of the genicular nerves might help to reduce radiation exposure, specifically when the procedure is being performed by novice practitioners with limited experience.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 it