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.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".