Prototype 3‐D Model of the Musculotendinous Architecture of Infraspinatus
Bibliographic record
Abstract
Muscle architecture, the arrangement of fibre bundles within the muscle volume, has important functional implications. Previous studies of infraspinatus (IS) muscle architecture have focused on dissection and photography of the superficial muscle layers rather than a volumetric analysis. The aim of this study was to develop a methodology to quantify the architectural parameters of the fibre bundles throughout the volume of IS. As a prototype, one formalin‐embalmed cadaveric specimen was used. The IS was exposed and each fibre bundle was meticulously dissected and digitized from end to end. The digitized data was imported and a 3D model of the fibre bundle architecture, as in situ , was constructed in Autodesk ® Maya ® 2012. Architectural parameters including fibre bundle length (FBL), pennation angle (PA), and physiological cross sectional area (PCSA) were computed. Based on the architectural parameters, muscular partitioning was determined. This technique successfully captured IS architecture throughout its volume. The IS was found to consist of 2 architecturally distinct regions, superior and inferior. The average measures for the superior and inferior regions were respectively: FBL 115.3mm; PA 22.2 ° ; PCSA 180.3mm 2 and FBL 84.9 mm; PA 15.9 ° ; PCSA 550.5 mm 2 . The results suggest that there is muscular partitioning of the IS. This methodology will form the basis of a continuing study to understand detailed IS architecture at the fibre bundle level.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".