Art skill-based rehabilitation training for upper limb sensorimotor recovery post-stroke: A feasibility study
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to assess the feasibility of delivering Art skill-based Rehabilitation Training (ART), a novel upper limb motor training program, to patients with stroke as an adjunct to standard care in an inpatient setting. DESIGN: Feasibility study. SETTING: Inpatient stroke rehabilitation unit at a university hospital. PARTICIPANTS: Thirty-eight patients admitted to a stroke rehabilitation unit with upper limb motor impairment were enrolled in the ART program facilitated by trained non-healthcare professionals between December 2017 and June 2021. INTERVENTION: The ART program included nine, one-hour sessions of supervised tracing and freehand drawing tasks completed with both hands. This program was intended to be delivered at a frequency of three times per week over a duration of 3 weeks or for the length of inpatient stay. MAIN OUTCOME MEASURES: Feasibility outcomes included ART program adherence, acceptability, and safety. RESULTS: Thirty-two (84%) participants with subacute stroke completed the ART program and 30 (79%) were included in the study analysis. Participants completed 93-100% of the ART tasks in a median [IQR] of 8 [6-10] ART sessions over a median [IQR] duration of 15 [7-19] days. ART program facilitators effectively provided upper limb assistance to patients with more severe upper limb impairments. Adherence and acceptability were high and no study-related adverse events occurred. CONCLUSION: The ART program was feasible to deliver and highly acceptable to patients with stroke. Further research is warranted to explore the impact of ART on upper limb sensorimotor function and use.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".