2 - Efficacy of ultrasound-guided radiofrequency ablation for genicular nerve in patients with chronic knee pain after total knee arthroplasty
Bibliographic record
Abstract
Background and aims: Despite a good outcome for many patients, approximately 20% of patients experience chronic pain after total knee arthroplasty (TKA). Radiofrequency ablation (RFA) for genicular nerve has recently gained popularity as an intervention for chronic knee pain in patients who have failed other conservative treatment. In the present study, we aimed to evaluate the efficacy of ultrasound-guided RFA for genicular nerve in patients with chronic knee pain after TKA.Methods: This study included 14 knees of 10 patients with chronic knee pain after TKA. Ultrasound-guided RFA was performed 80u2103 for 90 seconds. Pain intensity was assessed by Numeric Rating Scale (NRS 0-10) and knee disability was assessed by The Western Ontario McMaster OA Index (WOMAC). Each assessment was observed before procedure (baseline) and at 2, 4, 8, 12weeks after procedure. All measurement values were expressed as mean u00b1 SD. Outcome measures over time were evaluated using the repeated measurement general linear model. A value of p < 0.05 was considered statistically significant.Results: Baseline NRS was 7.1u00b11,1, and WOMAC was 76.9u00b110.3. Significant decrease in pain and significant improvement in knee function were observed at 2 weeks (NRS; 3.9u00b11.3, WOMAC; 51.6u00b112.6, p<0.05), 4weeks (NRS; 4.9u00b11.5, WOMAC; 51.7u00b112.4, p<0.05), and 8 weeks (NRS; 5.8u00b11.1, WOMAC; 53.7u00b110.8, p<0.05), compared to baseline values. No serious complication was found in any patient.Conclusions: Ultrasound-guided RFA for genicular nerve could be safe and beneficial treatment in the patients with chronic knee pain after TKA.
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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 | 0.002 |
| 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) | 0.001 | 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".