Evaluation of a novel nerve ablation technique to relieve lower back pain: a cadaveric feasibility pilot study
Bibliographic record
Abstract
INTRODUCTION: Radiofrequency ablation is a treatment for facetogenic low back pain that targets medial branches of lumbar dorsal rami to denervate facet joints. Clinical outcomes vary; optimizing cannula placement to better capture the medial branch could improve clinical outcomes. A novel parasagittal technique was proposed from an anatomic model; this technique was proposed to optimize capture of the medial branch. The anatomic feasibility of the novel technique has not been evaluated. OBJECTIVE: To simulate and evaluate the proposed parasagittal technique in its ability to achieve proper cannula placement and proximity of uninsulated cannula tips to the medial branches of the dorsal rami in cadaveric specimens. METHODS: Under fluoroscopic guidance, the parasagittal technique was used to place 14 cannulae targeting the lumbar medial branches of 2 cadavers. Meticulous dissection was undertaken to assess cannula alignment and measure proximities to target nerves with a digital caliper. RESULTS: The novel parasagittal technique was successfully performed in a cadaveric model in 12 of 14 attempts. The technique achieved close proximity of cannula tips to medial branches (0.8 ± 1.1 mm). In 2 instances, cannulae were placed unsuccessfully; in one instance, the cannula was too far anterior, and in the other, it was too far retracted. CONCLUSION: In this cadaveric simulation study, the feasibility of performing the parasagittal technique for lumbar radiofrequency ablation was evaluated. This study suggests that the parasagittal technique is a feasible option for lumbar medial branch radiofrequency ablation.
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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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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".