A Prospective Randomized Trial of Prognostic Genicular Nerve Blocks to Determine the Predictive Value for the Outcome of Cooled Radiofrequency Ablation for Chronic Knee Pain Due to Osteoarthritis
Bibliographic record
Abstract
Background and Objectives: Genicular nerve radiofrequency ablation is an effective treatment for patients with chronic pain due to knee osteoarthritis; however, little is known about factors that predict procedure success. The current study evaluated the utility of genicular nerve blocks to predict the outcome of genicular nerve cooled radiofrequency ablation (cRFA) in patients with osteoarthritis. Methods: This randomized comparative trial included patients with chronic knee pain due to osteoarthritis. Participants were randomized to receive a genicular nerve block or no block prior to cRFA. Patients receiving a prognostic block that demonstrated ≥50% pain relief for six hours received cRFA. The primary outcome was the proportion of participants with ≥50% reduction in knee pain at six months. Results: Twenty-nine participants (36 knees) had cRFA following a prognostic block, and 25 patients (35 knees) had cRFA without a block. Seventeen participants (58.6%) in the prognostic block group and 16 (64.0%) in the no block group had ≥50% pain relief at six months (P = 0.34). A 15-point decrease in the Western Ontario and McMaster Universities Osteoarthritis Index at six months was present in 17 of 29 (55.2%) in the prognostic block group and 15 of 25 (60%) in the no block group (P = 0.36). Conclusions: This study demonstrated clinically meaningful improvements in pain and physical function up to six months following cRFA. A prognostic genicular nerve block using a local anesthetic volume of 1 mL at each injection site and a threshold of ≥ 50% pain relief for subsequent cRFA eligibility did not improve the rate of treatment success.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".