Distribution, course, and spatial relationships of the saphenous nerve: A 3D neuroanatomical map for nerve stimulation
Bibliographic record
Abstract
The overall objective of this study was to construct a 3D neuroanatomical map of the saphenous nerve based on cartesian coordinate data to define its course in 3D space relative to bony and soft tissue landmarks. Ten lower limb embalmed specimens were meticulously dissected, digitized, laser scanned, and modelled in 3D. The course of the main branches, number of collateral branches, and relationship of saphenous nerve to the great saphenous vein were defined and quantified using the high-fidelity 3D models. In 60% of specimens, the saphenous nerve was found to have three branches in the leg, infrapatellar, anterior, and posterior. In 40% of specimens, the posterior branch was absent. Three landmarks were found to consistently localize the anterior branch: the medial border of tibia at the level of the tibial tuberosity, the medial border of tibia at the level of the mid-point of leg, and the mid-point of the anterior border of the medial malleolus. The posterior branch, when present, had variable branching patterns but did not extend as far distally as the medial malleolus in any specimen. Anatomically, the anterior and posterior branches at the level of the tibial tuberosity could be most advantageous for nerve stimulation due to their close proximity to the bifurcation of the saphenous nerve where the branches are larger and more readily localizable than distally. Additionally, the tibial tuberosity is a prominent landmark that can be easily identified in most individuals and could be used to localize the anterior and posterior branch using ultrasound or other imaging modalities. These findings will enable implementation of highly realistic computational models that can be used to simulate saphenous nerve stimulation using percutaneous and implanted devices.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".