Intramuscular Innervation of Infraspinatus: A 3‐D Modeling Study
Bibliographic record
Abstract
Neuromuscular partitioning is important in defining functional differences within a muscle volume. It has been shown that individual neuromuscular partitions may be differentially affected in pathology. Neuromuscular partitions are defined by independent innervation and architectural differences. Infraspinatus (IS) is a functionally important rotator cuff muscle where neuromuscular partitioning has not been well studied. The purpose is to investigate the intramuscular innervation patterns of IS. In this pilot study, 5 formalin‐embalmed cadaveric have been dissected, digitized, and modeled to date. The suprascapular nerve (SSN) was digitized sequentially in short segments and the data was modeled using Autodesk ® Maya ® 2012. The models were used to document the intramuscular innervation patterns/neuromuscular partitions. The SSN enters IS at the spinoglenoid notch as 1–3 nerves. In three specimens, one nerve entered the IS belly and divided into 3 main branches: (1) a superior branch (br) to superior part of the muscle belly; (2) a lateral br to upper inferior belly; (3) middle br to middle and lower inferior bellies. When SSN enters as 2–3 branches, a separate lateral and/or superior branch enter their respective regions. These results provide a detailed mapping of the intramuscular innervation of IS, providing evidence of neuromuscular partitioning within the IS muscle belly.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".