Transcranial Magnetic Stimulation Combined with Mirror Visual Feedback TrainingImproves the Clinical Effect of Unilateral Neglect after Stroke
Bibliographic record
Abstract
Objective To explore the clinical efficacy of transcranial magnetic stimulation combined with mirror visual feedback training system in the treatment of unilateral neglect after stroke. Methods A total of 60 post-stroke unilateral neglect patients in the rehabilitation medicine department of Shanghai Tongren Hospital from January 2021 to December 2022 were selected as the study objects.They were divided into control group and combined group by random number table method,with 30 cases in each group.The control group received transcranial magnetic stimulation therapy,and the combined group received transcranial magnetic stimulation combined mirror visual feedback training.Montreal cognitive assessment(MoCA) scale score,Chinese behavioral inattention test-Hong Kong(CBIT-HK) score,Catherine-Bogo Scale(CBS),and Modified Barthel index(MBI) score were compared between the two groups before treatment and 4 weeks after treatment. Results After treatment,MoCA score,CBIT-HK score and MBI score in combined group were higher than those in control group(P<0.05),the CBS score of the combined treatment group was lower than that of the magnetic stimulation group(P<0.05). Conclusion Transcranial magnetic stimulation combined with mirror visual feedback training can better improve post-stroke unilateral neglect.
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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 | 0.000 |
| 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) | 0.001 | 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".