Investigation of the static and dynamic musculotendinous architecture of supraspinatus
Bibliographic record
Abstract
To date, the architecture of supraspinatus (SP) and its relation to joint position has not been investigated. The purpose of this study was to quantify the dynamic architecture of the distinct regions of SP using ultrasound (US). Seventeen subjects (8 M/9 F), mean age 36.4 +/- 12.7 years, without tendon pathology were recruited. The SP was scanned in relaxed and contracted states. For the contracted state, SP was scanned with the shoulder actively abducted to 60 degrees and the glenohumeral joint in neutral rotation; 80 degrees external rotation; 80 degrees internal rotation. Fiber bundle length (FBL) and pennation angle (PA) of distinct regions, and muscle thickness were computed. Measurements of the posterior region were limited because of acromion shadowing. Parameters between regions and changes between relaxed and contracted states were analyzed using paired t-tests and repeated measures ANOVA (P < 0.05). On contraction in the anterior region, mean percentage of FBL shortening ranged between 9% and 21%. However, in the posterior region, shortening of approximately 2% only occurred in two of the three positions; lengthening of approximately 2.5% occurred in internal rotation. For the anterior region, the mean PA increased the least in the externally rotated position, and the mean PA of the middle part was smaller than the deep part for all states. Findings suggest changes in the architecture are not uniform throughout the muscle and joint position may play an important role in force production. The US protocol may serve as an outcome measure of adaptive changes of muscle function following surgery, training, and rehabilitation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".