Motor learning principles reported in stroke trials of upper limb task-oriented training: a scoping review
Bibliographic record
Abstract
OBJECTIVE: Upper limb task-oriented training (UL-TOT) is a complex intervention in which practice conditions related to motor learning principles are applied to enhance upper limb motor recovery after stroke. The Template for Intervention Description and Replication guidelines suggest that detailed reporting of a complex intervention is essential in published studies. Therefore, this review aimed to determine the extent to which practice conditions related to motor learning principles were reported in UL-TOT stroke clinical trials. METHODS: A comprehensive search was done using appropriate keywords in PubMed, CINAHL, Web of Science, Scopus and Cochrane databases from 2000 to 2024. Two reviewers independently conducted title screening, abstract screening and full-text evaluation based on the inclusion and exclusion criteria. A third reviewer resolved the conflicts between the two reviewers during the screening process. Finally, the articles that fulfilled the criteria were included for data extraction. RESULTS: 23 802 studies were retrieved, and 166 studies were retained. Practice conditions such as practice variability (98%), dosage (97%) and movement complexity (96%) were reported more frequently, task selection for practice (75%), challenging and progressive task practice (76%) were reported frequently, practice order (57%), practice distribution (51%), feedback type (44%) and timing (44%) were reported occasionally. Feedback frequency (37%) was reported rarely. CONCLUSIONS: Practice conditions such as practice variability, dosage, movement complexity, task selection, challenging and progressive task practice were reported consistently, while practice distribution, order and feedback were reported inconsistently. Developing a standard checklist for practice conditions related to motor learning principles can improve detailed reporting of practice conditions in future UL-TOT stroke clinical trials. This can help researchers replicate and reliably implement the intervention in specific populations and build on and create more effective interventions.
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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 | 0.057 | 0.250 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.009 |
| Bibliometrics | 0.018 | 0.022 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".