Radiofrequency Procedures to Relieve Chronic Knee Pain
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Chronic hip pain from osteoarthritis and other degenerative conditions is a common problem. A few publications have recently reported analgesic success of radiofrequency (RF) procedures on nerves innervating the hip, but interpretation is hampered by lack of clarity regarding indications, clinical protocols, anatomic targets, and longevity of benefit from RF procedures. METHODS: We reviewed the following medical literature databases for publications on RF procedures on the hip joint for chronic pain: MEDLINE, EMBASE, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, and Google Scholar through February 28, 2017. Existing knowledge on innervation of the hip joint was synthesized. Data on analgesic and functional outcomes and adverse effects measured at any time points following the interventions were also collected, analyzed, and reported in this narrative review. RESULTS: Fourteen publications on ablative RF treatments of innervation of the hip joint were identified. A high success rate of these procedures in relieving chronic pain of the hip joint was reported at 8 days to 36 months after the procedures, but none of the publications were randomized controlled trials. There was evidence for improvement in function and a lack of serious adverse events of RF treatments. CONCLUSIONS: Radiofrequency treatments for the sensory innervation of the hip joint have the potential to reduce pain secondary to degenerative conditions. Ongoing concerns remain regarding the anatomic targets, as well as quality, procedural aspects, and monitoring of outcomes in publications on this topic. Randomized controlled trials of high methodological quality are required to further elaborate the role of these interventions in this population.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".