Innervation pattern of the suprascapular nerve within supraspinatus: A three‐dimensional computer modeling study
Bibliographic record
Abstract
The relationship between the innervation pattern of the suprascapular nerve (SSN) and the muscle architecture of supraspinatus has not been thoroughly investigated. The supraspinatus is composed of two architecturally distinct regions: anterior and posterior. Each of these regions is further subdivided into three parts: superficial, middle and deep. The purpose of this study was to investigate the course of the SSN throughout the volume of supraspinatus and to relate the intramuscular branches to the distinct regions and parts of the supraspinatus. The SSN was dissected in thirty formalin embalmed cadaveric specimens and digitized throughout the muscle volume in six of those specimens. The digitized data were modeled using Autodesk(®) Maya(®) 2011. The three-dimensional (3D) models were used to relate the intramuscular innervation pattern to the muscle and tendon architecture defined by Kim et al. (2007, Clin Anat 20:648-655). The SSN bifurcated into two main trunks: medial and lateral. All parts of the anterior region were predominantly innervated by the medial trunk and its proximal and medial branches, whereas all parts of the posterior region predominantly by the lateral trunk and its posterolateral and/or posteromedial branches. The posterior region also received innervation from the proximal branch of the medial trunk in half of the specimens. These findings provide evidence that the anterior and posterior regions are distinct with respect to their innervation. The 3D map of the innervation pattern will aid in planning future clinical studies investigating muscle activation patterns and provide insight into possible injury of the nerve with supraspinatus pathology and surgical techniques.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".