Neuromuscular partitioning in the extensor carpi radialis longus and brevis based on intramuscular nerve distribution patterns: A three‐dimensional modeling study
Bibliographic record
Abstract
Differential activation of specific regions within a skeletal muscle has been linked to the presence of neuromuscular compartments. However, few studies have investigated the extra- or intramuscular innervation throughout the muscle volume of extensor carpi radialis longus (ECRL) and brevis (ECRB). The aim of this study was to determine the presence of neuromuscular partitions in ECRL and ECRB based on the extra- and intramuscular innervation using three-dimensional modeling. The extra- and intramuscular nerve distribution was digitized and reconstructed in 3D in all the muscle volumes using Autodesk Maya in seven formalin embalmed cadaveric specimens (mean age, 75.7 ± 15.2 years). The intramuscular nerve distribution was modeled in all the muscle volumes. ECRL was found to have two neuromuscular compartments, superficial and deep. One branch from the radial nerve proper was found to innervate ECRL. This branch was divided into anterior and posterior branches to the superficial and deep compartments, respectively. Five innervation patterns were identified in ECRB with partitioning of the muscle belly into two, three, or four compartments, in a proximal to distal direction depending on the number of nerve branches entering the muscle belly. The ECRL and ECRB both demonstrated neuromuscular compartmentalization based on intramuscular innervation. According to the partitioning hypothesis, a muscle may be differentially activated depending on the required function of the muscle, thus allowing multifunctional muscles to contribute to a variety of movements. Therefore, the increased number of neuromuscular partitions in ECRB when compared with ECRL could be due to the need for more differential recruitment in the ECRB depending on force requirements.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".