Feasibility and significance of stimulating interscapular muscles using transcutaneous functional electrical stimulation in able-bodied individuals
Bibliographic record
Abstract
OBJECTIVE: The study objective was to assess the feasibility of stimulating the lower trapezius (LT), the upper trapezius (UT) and serratus anterior (SA) muscles along with anterior or middle deltoid, using surface functional electrical stimulation (FES). The secondary aim was to understand the effects of LT, UT, and SA stimulation on maximum arm reach achieved in shoulder flexion and abduction. DESIGN: Single arm interventional study. SETTING: Inpatient Rehabilitation Hospital. PARTICIPANTS: Ten healthy volunteers. INTERVENTION: . (1) Active voluntary flexion or abduction, (2) FES for anterior deltoid for flexion or middle deltoid for abduction, and (3) FES for LT, UT, and SA along with anterior deltoid for flexion or middle deltoid for abduction. OUTCOME MEASURES: Maximum arm reach and percent angle relative to the voluntary movement were computed from motion capture data for each condition. Wilcoxon signed-rank test was used to compare the maximum reach between two FES conditions. RESULTS: The study results showed that all three interscapular muscles can be stimulated using surface FES. Maximum reach in abduction was greater for FES of middle deltoid along with the interscapular muscles (51.77° ± 17.54°) compared to FES for middle deltoid alone (43.76° ± 15.32°; Z = -2.701, P = 0.007). Maximum reach in flexion for FES of anterior deltoid, along with interscapular muscles, was similar to that during FES of anterior deltoid alone. CONCLUSION: Interscapular muscles can be stimulated using surface FES devices and should be engaged during rehabilitation as appropriate.
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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 | 0.002 |
| 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) | 0.003 | 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".