Effect of high-frequency mirror therapy for upper extremities after subacute stroke
Bibliographic record
Abstract
Objectives: This study aimed to investigate the effect of high-frequency mirror therapy (MT) on the upper extremities of patients suffering paresis following subacute stroke. Altogether, 50 subacute stroke patients with upper limb paresis whose strokes had occurred within 30–60 days of the start of this study were enrolled. The patients were randomly divided equally into groups assigned to conventional therapy (CT) alone or to CT plus mirror therapy (MT). All patients underwent CT training 40 min daily for 4 weeks. The MT group patients then continued an additional 20 min of shoulder, elbow, wrist, and finger MT, whereas the CT group continued with an additional 20 min of CT. Main outcome measures were the angles achieved during active shoulder flexion and abduction and wrist dorsiflexion, as well as upper-limb Fugl–Meyer Assessment (FMA) subscores. Results: For both the intention-to-treat and per-protocol analyses, the MT group showed significantly more improvement in active shoulder flexion range of motion than did the CT group. The FMA scores improved from before to after the start of the study in both groups, with no significant differences between the two groups. Conclusions: Application of MT at a high frequency probably has a positive effect on improving shoulder function in these subacute stroke patients. Thus, frequent MT application is essential for alleviating stroke-induced paralysis.
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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) | 0.001 | 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) | 0.002 | 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".