Effects of Neuromuscular Electrical Stimulation in Patients with Post-Stroke Related Dysarthria: A Double-Blinded, Phase-II Randomized Sham-Controlled Trial Protocol - ULYSSES Trial
Bibliographic record
Abstract
Introduction: Post-stroke individuals with dysarthria experience difficulties in producing speech due to muscle dysfunction. Neuromuscular electrical stimulation (NMES) can stimulate motor units and enhance their functionality. The objective of this study is to investigate the effects of NMES on speech intelligibility in patients with persistent dysarthria 3-6 months post-ischemic stroke. Methods: This study will be designed as a phase II, double-blinded, randomized, two-arm, parallel-group, superiority trial conducted at a single center. The target population will consist of post-stroke individuals with dysarthria, who will undergo randomization to receive either neuromuscular electrical stimulation (NMES) or sham-NMES. Both intervention groups will receive treatment sessions 5 days a week over a 4-week period. The sample size for this study will be 154 patients, recruited exclusively from a Rehabilitation Unit located in the United States. The primary outcome measure will focus on determining the mean difference in the FDA-2 intelligibility score between the two treatment groups. Secondary outcomes will involve evaluating the mean difference in the full FDA-2 score, as well as various subsets of the score, alongside an assessment of the participants’ health-related quality of life, utilizing the Stroke Impact Scale (SIS). Conclusion: To the best of our knowledge, this will be a comprehensive assessment of the potential benefits of NMES for post- stroke patients with dysarthria. Considering the positive impact of NMES on enhancing muscle functionality, it is plausible to anticipate its potential benefits in improving speech outcomes as well. Despite early studies indicating the safety and tolerability of NMES for various motor muscle conditions, there is limited data on its use in patients with dysarthria.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".