Evaluation of nerve capture using classical landmarks for genicular nerve radiofrequency ablation: 3D cadaveric study
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Radiofrequency (RF) denervation of the superolateral genicular nerve (SLGN), superomedial genicular nerve (SMGN) and inferomedial genicular nerve (IMGN) is commonly used to manage chronic knee joint pain. However, knowledge of articular branches captured, using classical landmarking techniques, remains unclear. In order to enhance and propose new RF procedures that conceivably capture a greater number of articular branches, more detailed cadaveric investigation is required. The objectives were to (1) determine which articular branches are captured or spared using classical landmarking techniques, and (2) evaluate the anatomical feasibility of classical landmarking techniques using three-dimensional (3D) modeling technology. METHODS: Ultrasound-guided classical superolateral/superomedial/inferomedial landmarking techniques were used to position RF cannulae in five specimens. The articular branches, bony and soft tissue landmarks, and cannula tip position, were meticulously dissected, digitized and modeled in 3D. Simulated lesions were positioned at the cannula tip, on the 3D models, to determine which articular branches were captured or spared. Capture rates of articular branches were compared. RESULTS: In all specimens, classical superolateral/superomedial techniques captured the transverse deep branches of SLGN and SMGN, and articular branches of lateral and medial nerve to vastus intermedius, while sparing distal branches of SLGN/SMGN. The inferomedial technique captured anterior branches of IMGN while sparing the posterior and inferior branches. CONCLUSIONS: This study provides anatomical evidence supporting the effectiveness of classical landmarking for genicular nerve ablation; however, each technique resulted in sparing of articular branches. The extensive innervation of the knee joint suggests the use of supplementary landmarks to improve capture rates and potentially patient outcomes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".