Ultrasound-Guided Genicular Nerve PulsedRadiofrequency Treatment For Painful KneeOsteoarthritis: A Preliminary Report
Bibliographic record
Abstract
Background: Genicular nerve ablation with radiofrequency (RF) has recently emerged as a promising treatment in the management of osteoarthritis related knee pain. To date, genicular nerve injections have been performed under fluoroscopic guidance. Objective: To evaluate the effect of ultrasound-guided genicular nerve pulsed RF treatment on chronic knee pain and function in patients with knee osteoarthritis. Study Design: Single-arm prospective study. Setting: University hospital and rehabilitation center in Turkey. Methods: A review was made of 29 patients with medial knee osteoarthritis who had undergone genicular nerve block in the previous 6 months. Patients with at least 50% reduction in the visual analog scale (VAS) score after genicular nerve block and with no on-going pain relief were selected for the study. Ultrasound-guided genicular nerve pulsed RF was applied to 15 knees of 9 patients. Pain and knee function were assessed with 100-mm VAS and Western Ontario and McMaster Universities (WOMAC) index throughout 3 months. Results: A significant reduction in VAS scores was detected over time after the pulsed RF procedure (f: 69.24, P < 0.01). There was a significant improvement in the WOMAC scores (f: 539.68 , P < 0.01). Limitations: The small number of participants, the lack of a control group, and short followup period were limitations of the study. Conclusions: Genicular nerve pulsed RF treatment has been found to be safe and beneficial in osteoarthritis related knee pain. Further studies with a larger population and randomized controlled study design are warranted to confirm the positive findings of this preliminary report. Key words: Knee pain, osteoarthritis, genicular nerve, ultrasonography, pulsed radiofrequency
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".