Functional electrical stimulation therapy improves grasping in chronic cervical spinal cord injury: Two case studies
Bibliographic record
Abstract
OBJECTIVE AND IMPORTANCE: To present case studies of two individuals with chronic cervical spinal cord injury (SCI) who participated in functional electrical stimulation (FES) therapy with the objective to restore voluntary grasp function. CLINICAL PRESENTATION: Both individuals (right hand dominant males, age 24 and 31) had a sustained a cervical SCI (C6 and C4-5, respectively) at least 8 years prior to participation in this study. INTERVENTION: Both individuals participated in an individualized FES therapy program for 6 weeks. FES therapy was administered through a regimen of three, one-hour sessions, per week for three months. A single arm of each participant (n = 2) was treated. FES therapy is an integrative intervention strategy combining muscle strengthening, functional movement training and stretching. The participant's hand movement abilities were assessed pre and post FES therapy using the Manual Muscle Test (MMT), a modified Sollerman Hand Function Test (mSHFT), and the Reach, Grasp, Transport and Release Task (RGTR). DISCUSSION: As the injuries of participants in the current study were chronic and thus neurologically stable, no spontaneous improvements/recovery in hand function was expected. However, FES as part of an integrated therapeutic approach affected restoration and improvement of hand function in both participants. CONCLUSION: The concurrent improvement in strength, integrated motor function and object contact following FES therapy, demonstrated that there is potential for affecting change in hand function of individuals with chronic SCI.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".