Neuromuscular Electrical Stimulation and Exercise for Reducing Trapezius Muscle Dysfunction in Survivors of Head and Neck Cancer: A Case-Series Report
Bibliographic record
Abstract
PURPOSE: Damage to the spinal accessory nerve (SAN) can result in denervation of the trapezius muscle in patients undergoing surgery for head and neck cancer. Trapezius denervation leads to muscle weakness and dysfunction that, for some patients, persists despite the return of conduction along the SAN. This prospective case series describes an intervention involving a combination of a novel type of neuromuscular electrical stimulation (NMES) with bilateral exercise. METHODS: Three survivors of head and neck cancer participated in the 6-week program. NMES was applied over the region of the SAN on the affected side while subjects performed bilateral voluntary scapular retraction and elevation exercises against resistance. The NMES was delivered using relatively wide pulse widths and high frequencies to enhance the electrically evoked sensory volley and was triggered by the onset of trapezius muscle activity on the non-affected side. Shoulder range of motion (ROM) assessments and patient-rated outcomes were administered at baseline and 6 weeks. RESULTS: All patients showed improvements in shoulder flexion and abduction ROM and reported reductions in pain and disability. CONCLUSIONS: This combination of NMES and bilateral exercise may prove to be an effective component of a comprehensive shoulder rehabilitation program for patients with persistent trapezius muscle dysfunction as a result of SAN damage.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".