Low frequency‐repetitive transcranial magnetic stimulation combined with <scp>Xingnao Kaiqiao</scp> acupuncture improves post‐stroke cognitive impairment and has better clinical efficacy
Bibliographic record
Abstract
BACKGROUND: Enhancing post-stroke cognitive impairment (PSCI) is a key aspect of prognosis for stroke patients. Low-frequency repetitive transcranial magnetic stimulation (LF-rTMS) is currently a widely utilised method for treating PSCI. With the increasing promotion of traditional Chinese medicine, Xingnao Kaiqiao (XNKQ) acupuncture has been progressively incorporated into clinical treatment. This paper observes the effect of LF-rTMS with XNKQ acupuncture on patients with PSCI. METHODS: Totally, 192 patients with PSCI were consecutively recruited and treated either with LF-rTMS and XNKQ acupuncture (observation group) or LF-rTMS only (control group) for 4 weeks. The pre- and post-treatment Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) scores, P300 latency and amplitude, inflammatory factor levels were compared and clinical efficacy was assessed. RESULTS: Both groups exhibited increased MMSE/MoCA scores, and P300 amplitude, and shortened P300 latency, and the observation group had higher scores and P300 amplitude, and shorter P300 latency than the control group. Both groups displayed decreased inflammatory factor levels (Tumour necrosis factor-α, interleukin (IL)-6, IL-10, IL-1β) after treatment, which were lower in the observation group than the control group. Inflammatory factor levels in PSCI patients were negatively interrelated with MMSE, MoCA score and P300 amplitude, and positively with P300 latency. The observation group showed an increased number of patients showing cured and significantly effective results, a decreased number of patients showing effective and invalid results, and an observably elevated total effective rate. CONCLUSION: LF-rTMS with XNKQ acupuncture can improve cognitive function and reduce inflammatory immune response, and has better clinical efficacy in PSCI patients.
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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) | 0.000 | 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".