Favorable Clinical Outcomes After Humeral Head Depressor Muscle Coactivation Training With EMG for Patients With Arthroscopic Rotator Cuff Repair: A Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: The coactivation (Co-A) of shoulder muscles that contribute to humeral head depression can lead to mechanical unloading of the subacromial structures during abduction and thus can be beneficial for patients with arthroscopic rotator cuff repair (ARCR). The present study aims to examine the effectiveness of humeral head depressor muscle Co-A (DM-Co-A) training on clinical outcomes in a sample of patients with ARCR. HYPOTHESIS: We hypothesized that DM-Co-A training with medioinferior vector during glenohumeral exercises can improve clinical results in the rehabilitation of ARCR. STUDY DESIGN: Randomized controlled single-blind study. LEVEL OF EVIDENCE: Level 1B. METHODS: A total of 27 patients who underwent ARCR after a medium-sized rotator cuff tear and completed their Phase 1 training with ≥80% compliance were included. Together with 14 weeks of conservative treatment (6 weeks of Phase 2 training and 8 weeks of Phase 3 training), synchronized "DM-Co-A Training" was applied to the treatment group with an electromyography (EMG) biofeedback (EMG-BF) device. Patients in the treatment group were asked to voluntarily activate the humeral head depressor muscles guided by visual and auditory feedback of the EMG-BF device during the Phase 2 and Phase 3 conservative treatment exercises performed by the control group. Demographic characteristics of the participants were recorded. Visual analog scale and universal goniometer were used to assess pre- and posttreatment pain severity and joint range of motion, respectively. The Disabilities of Arm, Shoulder and Hand Questionnaire, Revised Oxford Shoulder Score, Modified Constant-Murley Shoulder Score, and the Western-Ontario Rotator Cuff Index were used to assess functionality. RESULTS: < 0.05). CONCLUSION: A 14-week duration DM-Co-A with EMG may be beneficial in the postoperative rehabilitation of patients after ARCR.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".