Does Task-Oriented Practice Improve Upper Extremity Motor Recovery after Stroke? A Systematic Review
Bibliographic record
Abstract
Background. Stroke commonly affects upper extremity motor abilities, yet there has been very limited success in developing effective rehabilitation interventions to remediate motor impairments, particularly for the upper extremity. Objective. To determine if task-oriented practice administered soon after stroke is more effective than usual care in improving poststroke upper extremity motor recovery and to explore the optimal amount of practice. Methods. A systematic review of the literature was performed from 1950 to November 2012, to identify randomized controlled trials of task-oriented practice compared to usual care, or to different amounts of task-oriented practice to improve motor impairment and activity. Studies were excluded if specific types of interventions were used as comparators or if they were of poor methodological quality. Results. Six studies met the review criteria. Three of the six studies demonstrated a statistically significant effect of task-oriented practice. Study results could not be pooled because of a lack of homogeneity in populations and intervention. Conclusions. The results demonstrate that an increase in the amount of task-oriented practice after stroke may result in less upper extremity impairment; further research on both effect and required dosage is needed as results are inconsistent.
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 | 0.006 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.006 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".