Radiofrequency Denervation of the Lumbar Zygapophysial Joints—Targeting the Best Practice
Bibliographic record
Abstract
OBJECTIVE: Radiofrequency denervation of the zygapophysial (facet) joint is a frequently performed procedure for chronic low back pain. Several studies have shown considerable efficacy of the procedure, but none of the randomized controlled trials performed to date has used anatomically correct placement of radiofrequency cannula parallel to the target nerve. Three main techniques have been utilized for many years: North American, European, and Australian. Each has conceptual and technical limitations. This review analyzes these techniques and proposes a standardized technique of radiofrequency denervation of lumbar zygapophysial joints. DESIGN: Current techniques of radiofrequency neurotomy were analyzed with respect to anatomic and technical accuracy. Step by step guidelines were developed using a combination of previously described techniques along with newly elaborated technical hints and details. CONCLUSION: We believe that the technique using "tunnel vision" with anatomically appropriate cannula placement and use of a large-bore, curved needle with a 10-mm active tip may improve the results of radiofrequency denervation of lumbar zygapophysial joints. Providing a detailed description of an anatomically accurate technique should be of value to those seeking to perform this procedure in a valid manner.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".