The effects of multicomponent rehabilitation exercise plus soymilk on cognitive impairment and ischemic lesion growth in stroke patients: A randomized controlled trial
Bibliographic record
Abstract
OBJECTIVES: This study aimed to examine the effects of a multicomponent rehabilitation exercise, coupled with soymilk intake post-exercise, on cognitive impairment and ischemic lesion growth among acute stroke patients. METHODS: In a four-arm, single-blind, randomized clinical trial, 120 patients with acute stroke were randomly allocated to one of the following groups: 1) the MRE + soymilk, 2) the MRE, 3) the soymilk, and 4) the control group. Each group underwent their respective intervention for a continuous duration of 20 days. Cognitive impairment was assessed using the Montreal Cognitive Assessment, and the growth of ischemic lesions was evaluated through CT scans. RESULTS: The MRE combined with soymilk intervention demonstrated statistically significant improvements in cognitive impairment among acute stroke patients (χ² = 51.055, p = 0.000). Group differences began to emerge from Week, with improvements observed across all dimensions of cognitive function, except for abstraction. No significant differences were observed between groups in terms of ischemic lesion growth (χ² =0.934, p = 0.810). CONCLUSION: The incorporation of a multicomponent rehabilitation exercise combined with soymilk ingestion demonstrated effectiveness in alleviating cognitive impairment among acute stroke patients. Nevertheless, it did not influence the growth of ischemic lesions.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".