Genicular Nerve Block Versus Genicular Nerve Ablation for Knee Osteoarthritis: A Systematic Review of Randomized Controlled Trials and Retrospective Studies
Bibliographic record
Abstract
This systematic review aimed to compare the efficacy and safety of genicular nerve ablation and genicular nerve block (GNB) in pain control and functional improvement in knee osteoarthritis (OA) patients using a systematic review of randomized controlled trials (RCTs) and retrospective studies. We searched PubMed, Google Scholar, Cochrane, Science Direct, and Web of Science using specific keywords until April 2023. The primary outcome measures were visual analog scale (VAS) and numerical rating scale (NRS) scores for pain. The secondary outcome measures included functional outcomes assessed by the Western Ontario and McMaster Universities Arthritis Index (WOMAC) score and complications. Four RCTs and two comparative studies met the inclusion criteria. The analysis revealed that both genicular nerve ablation and nerve block effectively reduced pain and improved functionality. Ablation possibly provided more substantial and long-lasting effects than diagnostic blocks. However, the superiority of ablation compared to therapeutic block with steroids is still not conclusive in pain reduction. Functional capacity improvements were comparable between ablation and therapeutic block. Adverse events were minimal and transient.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.009 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".