Task-Specific, Patient-Driven Neuroprosthesis Training in Chronic Stroke: Results of a 3-Week Clinical Study
Bibliographic record
Abstract
OBJECTIVE: We examined the efficacy of a clinically based regimen in which a woman 16 months' poststroke participated in daily practice sessions of valued activities of daily living (ADLs). A unique aspect of this intervention was that it was largely patient driven, with the patient practicing ADLs while wearing an electrical stimulation neuroprosthesis. METHOD: The Fugl-Meyer Assessment (FM), Action Research Arm Test (ARA), Arm Motor Activity Test (AMAT), and Canadian Occupational Performance Measure (COPM) were administered before intervention. Therapy consisted of 3-hr ADL training sessions every weekday during a 3-week period using a neuroprosthesis featuring functional electrical stimulation during treatment sessions. One week after the end of the treatment phase, the FM, ARA, AMAT, and COPM were again administered. RESULTS: The patient exhibited reduced impairment (FM score change from 31 to 35), decreased time needed to complete AMAT tasks (from 998 s to 558 s), and increased ARA score (from 27 to 31). CONCLUSIONS: Clinically meaningful changes were realized with distant or minimal therapist supervision, making this regimen a practical and efficacious alternative.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".