The Effectiveness of Pulsed Radiofrequency on Joint Pain: A Narrative Review
Bibliographic record
Abstract
BACKGROUND: Pulsed radiofrequency (PRF) stimulation has been safely and effectively applied for controlling various types of pain. PURPOSE: We reviewed the literature on the efficacy of PRF for controlling pain in joint disorders. METHODS: We searched PubMed for papers published prior to September 7, 2019, that used PRF to treat pain due to joint disorders. The key search phrases for identifying potentially relevant articles were (PRF AND joint) OR (PRF AND arthritis) OR (PRF AND arthropathy). The following inclusion criteria were applied for the selection of articles: (1) patients' pain was caused by joint disorders; (2) PRF stimulation was applied to manage joint-origin pain; and (3) after PRF stimulation, follow-up evaluation was performed to assess the reduction in pain intensity. Moreover, joints with more than 3 reported PRF studies were included in our review. RESULTS: The primary literature search yielded 141 relevant papers. After reading their titles and abstracts and assessing their eligibility based on the full-text articles, we finally included 34 publications in this review. Based on the positive therapeutic outcomes of previous studies, PRF stimulation seems to be an effective treatment for cervical and lumbar facet, sacroiliac, knee, and glenohumeral joint pain. PRF appears to be beneficial. For confirmation of the effectiveness of PRF on joint pain, more high-quality studies are needed. CONCLUSIONS: Our review provides insights on the degree of evidence according to pain in each joint, which will help clinicians make informed decisions for using PRF stimulation in various joint pain conditions.
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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".