A feasibility study on the use of cranial nerve non-invasive neuromodulation to improve affected arm function in people in the chronic stage of a stroke
Bibliographic record
Abstract
BACKGROUND: Chronic stroke survivors are often left with residual arm muscle weakness impeding arm function, daily life activities and quality of life. Exercise is one of the main post-stroke interventions to improve arm function, with cranial nerve non-invasive neuromodulation (CN-NINM) emerging as a potentially interesting complementary therapy to enhance its benefits. Only one study has evaluated the impact of CN-NINM combined with a lower-limb training program on improved balance in subacute stroke survivors. The aim of this study was to assess the feasibility and explore the effects on motor function of an arm strengthening program combined with CN-NINM in chronic stroke survivors (> 6 months). METHODS: Twelve (12) participants (69 ± 11 years) took part in this feasibility study. Recruitment and drop-out rates, number of people who elected not to participate, adherence and adverse events were collected to assess feasibility. The effects of CN-NINM + exercise on motor function were evaluated by changes in arm motor function, measured using the Fugl-Meyer Assessment (FMA), and functional performance, evaluated through the Wolf Motor Function Test (WMFT), following a 4-week arm strengthening program (60 min, 3 sessions/week) combined with CN-NINM (tongue stimulation, 20 min at a comfortable intensity). Descriptive and non-parametric statistics (Wilcoxon signed-ranks test) were used to describe feasibility data and explore CN-NINM effects. RESULTS: Feasibility was confirmed with a recruitment rate of 1.3 person/month, no dropout, a 100% adherence rate, and no serious adverse events. A significant gain in FMA (p = 0.003) with a trend for WMFT (P = 0.11) were noted post-intervention. CONCLUSION: This study suggests that CN-NINM combined with an arm strengthening program is feasible and may improve arm function in chronic stroke survivors. Further research is needed to validate the results. TRIAL REGISTRATION: This clinical trial was registered on ClinicalTrials.gov (NCT05370274) on April 27, 2022.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".