Stochastic Resonance Stimulation for Upper Limb Rehabilitation Poststroke
Bibliographic record
Abstract
OBJECTIVES: Previous studies have shown that subthreshold electrical or mechanical noise can reduce the sensory threshold and impart short-term improvements in sensorimotor function. We undertook this study to examine the effects of combined subsensory electrical and vibratory stimulation in conjunction with exercise training on long-term motor performance. DESIGN: Thirty subjects were recruited from adult community-dwelling stroke survivors with residual hemiparesis. Subjects were screened for residual motor ability using a functional task, and those who functioned below this level were excluded. All subjects had a history of a single unilateral ischemic or hemorrhagic stroke at least 6 mos before study entry and were not actively receiving occupational or physical therapy. Subjects were stratified by baseline upper extremity Fugl-Meyer (UEFM) (more impaired [28-35] and less impaired [36-55]) and were randomized to one of two groups: treatment (stochastic resonance stimulation [plus over minus sign] exercise: 15 subjects) and control (sham stimulation [plus over minus sign] exercise: 15 subjects). RESULTS: No significant difference was found between the stochastic resonance treatment and control group in the UEFM or in any of the secondary measures. The combined group showed modest improvements in UEFM from baseline to completion of therapy (mean improvement, 2.6 points) (P = 0.004); however, these improvements declined by 1-mo follow-up to 1.5 points (P = 0.055). No change in sensory function was detectable. CONCLUSIONS: Stochastic resonance therapy combined with occupational therapy was no more effective than occupational therapy alone in restoring sensorimotor performance. Other stochastic resonance stimulation montages or protocols might prove more effective.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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) | 0.002 | 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".