Ultrasound-Guided Cervical Medial Branch Radiofrequency Neurotomy
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Novel multitined cannulae constitute an attractive option for ultrasound-guided radiofrequency neurotomy of cervical medial branches. The deployment tines increase the cannula's active area, thus altering its lesion size. Despite their theoretical benefits, multitined cannulae have not been assessed. In this bench study, we sought to investigate the lesions produced by a standard 18-gauge and 2 commercially available multitined deployment cannulae. We created ex vivo models to evaluate lesion morphology at a periosteal interface using approach angles likely to be encountered during an ultrasound-guided technique. METHODS: Two ex vivo models were assembled using chicken breast tissue and bovine tibia. Monopolar lesions were carried out with 3 commercially available cannulae (18-gauge curved [S], 17-gauge with laterally deploying tines [N], and 18-gauge with distally deploying tines [T]). All cannulae were positioned at 0, 25, 45, and 90 degrees to the periosteal plane. For each angulation and cannula, 2 series of measurements were recorded to document lesion morphology in the axial and sagittal planes. Data collected included the lesion's surface area, shape, and dimensions relative to the needle tip. RESULTS: A total of 240 lesions were analyzed. The performances of S and N cannulae were significantly affected by approach angle, with lesion size decreasing as the angle increased. In contrast, T cannulae displayed similar lesion surface areas at 0 and 90 degrees. The multitined N and T cannulae produced the largest lesions at 0/25 and 90 degrees, respectively. Lesion height varied inversely according to approach angle for S and N cannulae, whereas T cannulae displayed stable characteristics. CONCLUSIONS: Unlike their S and N counterparts, T cannulae demonstrated stable lesion characteristics at varying approach angles.
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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 | 0.000 |
| 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".