Lumbar facet joint denervation targeting the medial branch in the sub-mammillary fossa: An anatomical optimization study
Bibliographic record
Abstract
Introduction: Recent anatomical studies have identified the sub-mammillary fossa as a potential target site to extend the length and more reliably capture the medial branch during lumbar facet joint denervation. Although a clinical case series was published describing positive outcomes targeting the sub-mammillary fossa, the ideal location for radiofrequency cannula placement has not been assessed. Further anatomical investigation of this novel technique is warranted to refine fluoroscopic landmarks for optimal placement. Methods: Twelve cannulae were placed under fluoroscopic guidance targeting the L3, L4, & L5 medial branches in 2 embalmed cadaveric specimens. Dissection, digitization, and high-fidelity 3D modelling methodology was used to identify fluoroscopic landmarks. Lesion simulation was performed on 3D models to analyze nerve capture. Results: In 5 of 12 placements (41.7 %), the medial branch capture rate was classified as "complete," as the simulated lesion overlapped with the medial branch trunk or all of its distal branches. In 4 of 12 placements (33.3 %), the nerve capture rate was "partial" with at least one distal branch found beyond the boundary of the simulated lesion. In the remaining 3 placements (25.0 %), the capture rate was classified as "none," as the medial branch trunk and all distal branches transited beyond the simulated lesion boundary. Refined fluoroscopic landmarks proposed were the lateral boundary of mammillary process (AP view); the mamillo-accessory notch/inferior boundary of facet joint line (oblique view); and the inferior aspect of the mammillary process (lateral view). Conclusions: This anatomy optimization study used dissection, imaging correlation, and high-fidelity modelling to assess cannula placement for capture of the medial branch at the sub-mammillary fossa for lumbar facet joint denervation. Based on the present analysis, refined fluoroscopic landmarks were proposed for further investigation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".