Change in the Pathologic Supraspinatus: A Three-Dimensional Model of Fiber Bundle Architecture within Anterior and Posterior Regions
Bibliographic record
Abstract
Supraspinatus tendon tears are common and lead to changes in the muscle architecture. To date, these changes have not been investigated for the distinct regions and parts of the pathologic supraspinatus. The purpose of this study was to create a novel three-dimensional (3D) model of the muscle architecture throughout the supraspinatus and to compare the architecture between muscle regions and parts in relation to tear severity. Twelve cadaveric specimens with varying degrees of tendon tears were used. Three-dimensional coordinates of fiber bundles were collected in situ using serial dissection and digitization. Data were reconstructed and modeled in 3D using Maya. Fiber bundle length (FBL) and pennation angle (PA) were computed and analyzed. FBL was significantly shorter in specimens with large retracted tears compared to smaller tears, with the deeper fibers being significantly shorter than other parts in the anterior region. PA was significantly greater in specimens with large retracted tears, with the superficial fibers often demonstrating the largest PA. The posterior region was absent in two specimens with extensive tears. Architectural changes associated with tendon tears affect the regions and varying depths of supraspinatus differently. The results provide important insights on residual function of the pathologic muscle, and the 3D model includes detailed data that can be used in future modeling studies.
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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 | 0.000 |
| 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".