Targeted Muscle Reinnervation in the Lower Leg: An Anatomical Study
Bibliographic record
Abstract
BACKGROUND: Targeted muscle reinnervation reroutes the ends of cut nerves to reinnervate small motor nerves of nearby muscles, with the goal of reducing neuroma pain and/or improving prosthesis function. Anatomical roadmaps for targeted muscle reinnervation have been established in the upper extremity and thigh, but not for the lower leg. METHODS: The major branch points of motor nerves and the motor entry points to muscles of the leg were dissected in five cadaver specimens. Leg length was defined as distance from the lateral femoral condyle to the lateral malleolus. The distances from the lateral femoral condyle to major branch points and motor entry points were recorded as percentages of leg length to identify targets for targeted muscle reinnervation. RESULTS: The tibialis anterior and extensor digitorum longus were both acceptable targets in the anterior compartment, with an average 4.4 motor entry points within 10 to 80 percent and 3.0 motor entry points within 20 to 80 percent leg length, respectively. The peroneus longus was the best target in the lateral compartment, with an average 5.8 motor entry points within 20 to 70 percent leg length. The gastrocnemius and soleus were both acceptable targets in the superficial posterior compartment, with an average 4.4 motor entry points within 0 to 40 percent and 6.2 motor entry points within 20 to 80 percent leg length, respectively for each muscle. The flexor digitorum longus was the best target in the deep posterior compartment, with an average 6.0 motor entry points within 30 to 90 percent leg length. CONCLUSIONS: Targeted muscle reinnervation is technically feasible in the lower leg. This cadaveric study provides a roadmap for incision placement and identification of motor nerve targets.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".