Impact of task-oriented training based on acupuncture rehabilitation on upper extremity function and quality of life of patients with early stroke
Bibliographic record
Abstract
BACKGROUND Eighty percent of stroke patients develop upper limb dysfunction, especially hand dysfunction, which has a very slow recovery, resulting in economic burden to families and society. AIM To investigate the impact of task-oriented training based on acupuncture therapy on upper extremity function in patients with early stroke. METHODS Patients with early stroke hemiplegia who visited our hospital between January 2021 and October 2022 were divided into a control group and an observation group, each with 50 cases. The control group underwent head acupuncture plus routine upper limb rehabilitation training (acupuncture therapy). In addition to acupuncture and rehabilitation, the observation group underwent upper limb task-oriented training (30 min). Each group underwent treatment 5 d/wk for 4 wk. Upper extremity function was assessed in both groups using the Fugl-Meyer Assessment-Upper Extremity (FMA-UE), Wolf Motor Function Rating Scale (WMFT), modified Barthel Index (MBI), and Canadian Occupational Performance Measure (COPM). Quality of life was evaluated using the Short-Form 36-Item Health Survey (SF-36). Clinical efficacy of the interventions was also evaluated. RESULTS Before intervention, no significant differences were observed in the FMA-UE, MBI, and WMFT scores between the two groups (P > 0.05). After intervention, the FMA-UE, WMFT, MBI, COPM-Functional Mobility and Satisfaction, and SF-36 scores increased in both groups (P < 0.05), with even higher scores in the observation group (P < 0.05). The observation group also obtained a higher total effective rate than the control group (P < 0.05). CONCLUSION Task-oriented training based on acupuncture rehabilitation significantly enhanced upper extremity mobility, quality of life, and clinical efficacy in patients with early stroke.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 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".