Effectiveness of pulsed radiofrequency treatment on cervical radicular pain
Bibliographic record
Abstract
BACKGROUND: Cervical radicular pain is a challenging medical problem in terms of therapeutic management. Recently, pulsed radiofrequency (PRF) stimulation on the dorsal root ganglion (DRG) has been used to control several types of chronic pain. However, its effect on cervical radicular pain is still not well studied. To conduct a meta-analysis of available clinical studies on PRF treatment in patients with cervical radicular pain induced by cervical spine disease that was not responsive to other conservative treatments. METHODS: A comprehensive database search was conducted on PubMed, Embase, Cochrane Library, and SCOPUS. We included studies published up to August 31, 2017, that fulfilled our inclusion and exclusion criteria. The pain degrees measured using visual analog scale (VAS) at pretreatment and after PRF on the DRG were collected for the meta-analysis. The Cochrane Collaboration's Handbook and Newcastle-Ottawa scale were used for the methodological quality assessments of included studies. The meta-analysis was performed using the Comprehensive Meta-analysis Version 2. RESULTS: A total of 67 patients from one RCT study, 2 prospective observational studies, and one retrospective study were included in this meta-analysis. The pooled data of the 4 included studies showed that overall VAS after the PRF treatment was significantly reduced (P ≤ .001). In the subgroup analysis according to follow-up evaluation time points, the pain was significantly reduced at 2 weeks, 1 month, 3 months, and 6 months after the procedure (2 weeks: P = .02; 1, 3, and 6 months: P < .001). CONCLUSION: According to the results of the meta-analysis, the use of PRF on the DRG is effective for alleviating cervical radicular pain, which was unresponsive to oral medications, physical therapy, or epidural steroid injection.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.014 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".