In-Home Tele-Rehabilitation Improves Tetraplegic Hand Function
Bibliographic record
Abstract
BACKGROUND: Spinal cord injury (SCI) survivors with tetraplegia have great difficulty performing activities of daily living (ADLs). Functional electrical stimulation (FES) combined with exercise therapy (ET) can improve hand function, but delivering the treatment is problematic. OBJECTIVE: To compare 2 ET treatments delivered by in-home tele-therapy (IHT). METHODS: Each treatment involved ET, tele-supervised 1 h/d, 5 d/wk for 6 weeks. Treatment 1: "conventional ET" comprised strength training, computer games played with a trackball, and therapeutic electrical stimulation (TES). Treatment 2: "ReJoyce ET" comprised FES-ET on a workstation, the Rehabilitation Joystick for Computerized Exercise (ReJoyce) with which participants played computer games associated with ADLs. Participants were block-randomized into group 1 receiving conventional ET first, followed by 1-month washout, and then ReJoyce ET and group 2 in reverse order. In all, 13 participants took part, 5 completing the study with both hands, such that both groups had a sample size of 9. PRIMARY OUTCOME MEASURE: Action Research Arm Test (ARAT). SECONDARY OUTCOME MEASURES: grasp and pinch forces and the ReJoyce automated hand function test (RAHFT). RESULTS: ARAT scores improved more after ReJoyce ET (13.0% ± 9.8%) than after conventional ET (4.0% ± 9.6%; F = 10.6, P < .01). RAHFT scores also improved more after ReJoyce ET (16.9% ± 8.6%) than conventional ET (3.3% ± 10.2%; F = 20.4, P < .01). CONCLUSIONS: FES-ET on a workstation, supervised over the Internet, is feasible and may be effective for patients who can meet the residual motor function requirements of our study.
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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 | 0.000 | 0.001 |
| 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) | 0.005 | 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".