The effect of repeated transcranial magnetic stimulation combined with cognitive rehabilitation training on post-stroke cognitive impairment.
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to explore the effect of repetitive transcranial magnetic stimulation (rTMS) combined with cognitive rehabilitation training on post-stroke cognitive impairment (PSCI). PATIENTS AND METHODS: We retrospectively reviewed clinical data from 119 patients with PSCI admitted to our hospital from December 2021 to April 2023, of which 58 received pure cognitive rehabilitation training (control group) and 61 received rTMS combined with cognitive rehabilitation training (observation group). We calculated measures of cognitive function rehabilitation, daily living activity abilities, latency and amplitude of P300 wave of evoked potential, and serum biochemical index levels before and after the intervention in the two groups. RESULTS: After the intervention, the scores of the Montreal Cognitive Assessment (MoCA) scale and Rivermead behavioral memory test (RBMT) had improved in the two groups. Also, the Modified Barthel Index (MBI) scores of the two groups increased after the intervention. The P300 wave latencies in both groups decreased and their amplitudes increased after the intervention. The levels of serum neurotrophin-3 (NT-3) and brain-derived neurotrophic factor (BDNF) in the two groups were higher, and the levels of brain glial fibrillary acidic protein (GFAP) were lower after the intervention. All these improvements were more marked in the observation group than in the control group (all p<0.05). CONCLUSIONS: Compared with simple cognitive rehabilitation training, the training combined with rTMS was more effective at restoring cognitive function, improving daily living activity abilities, and improving the treatment outcome of patients with PSCI.
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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 | 0.001 |
| 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".