Predictive factors of upper limb motor recovery for stroke survivors admitted to a rehabilitation program
Bibliographic record
Abstract
BACKGROUND: Various factors may interact with functional gains from upper limb motor training in patients with stroke. AIM: This study aimed to explore the predictors of upper limb motor recovery in patients with stroke who were admitted to a rehabilitation program. DESIGN: A retrospective, longitudinal observational study was conducted to evaluate the change in Fugl-Meyer assessment upper extremity Score (FMA-UE) at admission and 15 and 30 days after admission. SETTING: Setting of the study was a rehabilitation hospital. POPULATION: Patients received rehabilitation training during the study period. METHODS: Demographic information and clinical factors were collected as independent variables. Longitudinal analysis of UE motor recovery measured by FMA-UE over time was performed using the mixed-effects model. RESULTS: Data from 110 participants were included. FMA-UE score showed significant increase (β=4.12, P<0.001). Cognitive functions assessed by the Montreal Cognitive Assessment (MoCA) positively correlated with the improvement in UE functions (β=0.13, P<0.001), while time since stroke negatively correlated with improvement across time (β=-0.05, P=0.019). Patients with subcortical lesions improved faster than those with mixed cortical and subcortical lesions did (difference in slope =2.83, P=0.001). Improvement in patients with moderately impaired UE motor functions was faster than in those with severely impaired UE motor functions (difference in slope =2.74, P=0.016). Severity of hemiplegia, MoCA, and time since stroke were significant predictors in multivariable, mixed-effects models. CONCLUSIONS: Initial motor and cognitive impairments may be associated with UE motor recovery in patients admitted to a rehabilitation program. CLINICAL REHABILITATION IMPACT: Early assessments of motor and cognitive impairments after stroke would contribute to the prediction of UE motor recovery in patients admitted to a rehabilitation program. The information would also help the stratification of patients for poststroke upper limb rehabilitation trials.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".