Magnetic Positioning System and Ultrasound Guidance for Lumbar Zygapophysial Radiofrequency Neurotomy
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Chronic low back pain related to degenerative spondylosis is commonly managed by the radiofrequency ablation of sensory nerves. Fluoroscopic guidance has been considered mandatory to ensure placement of the active tip of the cannula parallel to the nerve to provide adequate neurolysis. Conversely, analgesic (or diagnostic) blockade is usually accomplished by placing the needle perpendicular to the nerve using either fluoroscopy or ultrasound (US) guidance. The recently introduced disposable equipment of internally cooled radiofrequency allows the denervation procedure to be performed similarly to the routine diagnostic block. Consequently, US may now potentially be used for image-guided radiofrequency neurotomy. We sought to compare the accuracy using a novel US-based technique with the traditional fluoroscopy-guided placement. METHODS: The proof of concept study was performed using a magnetic positioning US-guided system. The precision of needle placements was compared with the standard fluoroscopic guidance. The primary outcome of this study was defined as the procedural accuracy. Procedural and radiation exposure time was also recorded. In addition, projected operational expenses were calculated. RESULTS: Ultrasound-guided procedural accuracy reached 97%. Both the imaging and procedure times were similar between the 2 forms of imaging guidance. Of significant importance, the US-guided approach (no radiation exposure) was quantitatively advantageous over fluoroscopy-guidance, which required an average of 170 seconds of radiation per procedure. Thus, the US method seems to be cost effective. CONCLUSIONS: A magnetic positioning system allows accurate and quick US-guided placement of radiofrequency cannula to the desired anatomical targets, sparing patients and personnel from exposure to ionized radiation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".