Radiofrequency ablation versus intra-articular mesenchymal stem cell injection for knee osteoarthritis: a systematic review and network meta-analysis
Bibliographic record
Abstract
BACKGROUND: Knee osteoarthritis (OA) is a prevalent degenerative disease and causes disability, pain and imposes a substantial burden on patients. Conventional treatments for knee OA show limited effectiveness. Consequently, innovative treatments, such as radiofrequency ablation (RFA) and intra-articular mesenchymal stem cells (IA MSC), have gained attention for addressing these limitations. OBJECTIVE: We compared the efficacy of RFA and IA MSC for knee OA through a network meta-analysis (NMA). EVIDENCE REVIEW: A literature search was conducted using PubMed, MEDLINE, Embase, Cochrane Library, Web of Science and handsearching. Randomized controlled trials (RCTs) comparing RFA or IA MSC to conventional treatments for knee OA were included. The primary outcomes comprised the pain score and the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC). The clinical outcomes were compared using a frequentist approach, and the treatments were ranked using the surface under the cumulative ranking curve (SUCRA) values. FINDINGS: We included 34 RCTs (n=2371). Our NMA revealed that RFA and IA MSC were significantly more effective than conventional treatments in managing pain at both 3 and 6 months with moderate certainty. Specifically, RFA demonstrated the highest SUCRA values, indicating its superior efficacy. For WOMAC scores, both RFA and MSC showed significant improvements at 3 months, with RFA maintaining its lead at 6 months, although MSC did not display significant superiority at this stage. CONCLUSIONS: This analysis suggests that RFA and MSC are resilient treatment options in knee OA. Despite some study heterogeneity, these treatments consistently outperformed conventional treatments, particularly in the short to mid-term, although with varying levels of certainty in their efficacy. PROSPERO REGISTRATION NUMBER: CRD42023492299.
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 | 0.015 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.032 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".