Evaluation of supraspinatus strengthening exercises based on fiber bundle architectural changes
Bibliographic record
Abstract
Supraspinatus strengthening is an integral part of rehabilitation programs. However, there is disparity regarding the best exercise. Thus, we investigated the effects of supraspinatus strengthening exercises on muscle fiber bundle architecture using real-time ultrasound. Participants were randomized into full-can (FC; n = 12), empty-can (EC; n = 11,) and prone horizontal abduction (PHA; n = 11) groups and performed three sessions/week for 8 weeks. Each session involved four sets of exercise for weeks 1-4 and six sets for weeks 5-8 with eight repetitions/set. Images of supraspinatus were captured in 0° (relaxed) and 60° (contracted) glenohumeral abduction. Fiber bundle length (FBL), pennation angle, and muscle thickness were measured at beginning, mid, and end of training. Maximum isometric abduction strength was measured in full-can, empty-can, and prone horizontal abduction positions using a hand-held dynamometer. FBL decreased after 4 weeks in FC [relaxed: Δ = -0.54 cm, P = 0.001, d = 0.92; contracted: Δ = -0.27 cm, P = 0.008, d = 0.27] and EC [relaxed: Δ = -0.59 cm, P = 0.008, d = 0.86; contracted: Δ = -0.36 cm, P = 0.013, d = 0.59], but not in PHA [relaxed: Δ = -0.11, P = 0.121, d = 0.31; contracted: Δ = -0.06 cm, P = 0.486, d = 0.13]. Strength increased significantly from week 4-8 only in PHA (Δ = +0.96 kg, P = 0.004, d = 0.47) when tested in prone horizontal abduction position. Maintenance of FBL with increased strength indicates prone horizontal abduction may be a better exercise to strengthen supraspinatus.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".