Association between self‐perceived activity performance and upper limb functioning in subacute stroke
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: This study aimed to investigate to what extent upper limb (UL) motor impairment, trunk compensation, and activity performance are related to self-perception of UL activity performance in subacute stroke. METHODS: This was a prospective observational study. Twenty-four adults with subacute stroke (age: 65.4 ± 10.8 years) underwent clinical and kinematic assessments at baseline (33.9 ± 5.2 days after stroke onset) and 4 weeks after the baseline. The clinical assessment included the UL Fugl-Meyer motor assessment (FMA), Simple Test for Evaluating hand Function (STEF), and the performance and satisfaction scores of the Canadian Occupational Performance Measure (COPM). The kinematic measurement was performed using a motion capture system during a standardized reach-to-grasp task. Endpoint performance variables and trunk displacement were calculated as kinematic outcomes. An inpatient rehabilitation program of 3 h/day was provided every day for 4 weeks between the two measurement points. The relationships between COPM scores and clinical/kinematic outcomes were examined by multiple regression analysis. Significance levels of p < 0.05 were used. RESULTS: = 0.426, p = 0.001), while the change in UL FMA was not. DISCUSSION: The changes in activity performance and trunk compensation were related to improved self-perception of UL activity performance. Therapeutic management for activity performance and trunk compensation may be important for improving self-perception of UL activity performance after stroke.
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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".