Comparing preference related to comfort in torque-matched muscle contractions between two different types of functional electrical stimulation pulses in able-bodied participants
Bibliographic record
Abstract
CONTEXT/OBJECTIVE: Functional electrical stimulation (FES) is commonly used in rehabilitation to generate electrically-induced muscle contractions. FES has been shown to assist in the recovery of voluntary motor functions after stroke or spinal cord injury. However, discomfort associated with FES can motivate patients to withdraw their participation from FES therapy despite its benefits. To address this issue, a functional electrical stimulator, called MyndMove™ (MyndTec Inc., Canada), has been developed to generate more comfortable contractions than conventional stimulators. DESIGN: Cross-sectional, interventional, with two treatment arms. SETTING: A laboratory within a rehabilitation center. PARTICIPANTS: Twelve able-bodied participants. INTERVENTION: FES delivered with two different stimulators, MyndMove™ and Compex Motion (Compex, Switzerland), during muscle contractions of high, moderate and low stimulation intensity. OUTCOME MEASURES: Comfort-related preference to a given stimulator and the discomfort score rated through a Numeric Rating Scale (NRS-101) for both stimulators. RESULTS: Participants perceived a reduction in discomfort during high-intensity stimulation generated using MyndMove™. In addition, MyndMove™ stimulations were preferred in 60% of all contractions. The reduction in discomfort associated with MyndMove™ might be due the fact that MyndMove™ delivers less charge to generate contractions of equivalent intensity, compared to Compex Motion. CONCLUSION: Reducing discomfort during FES may help in generating stronger and more clinically useful contractions, increasing accessibility of FES therapy to include individuals with low tolerance to FES.
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 | 0.001 | 0.003 |
| 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) | 0.004 | 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".