Effects of different acupuncture treatment methods on post-stroke cognitive impairment: study protocol for a multicenter randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: Cognitive impairment is a common dysfunction after stroke that seriously affects the overall recovery of patients. Cognitive rehabilitation training is currently the main treatment to improve cognitive function, but its curative effect is limited. Acupuncture is a core component of traditional Chinese medicine (TCM), and some previous clinical studies have shown that it might be effective in treating post-stroke cognitive impairment (PSCI), but further evidence from large-sample studies is needed. The overall objective of this trial is to obtain further data to develop an optimized acupuncture treatment for PSCI by comparing the effects of different acupuncture treatment methods on cognitive function in PSCI patients. METHODS/DESIGN: In this multicenter, prospective, randomized controlled trial, 206 eligible stroke inpatients who meet the trial criteria will be randomly assigned to 2 groups: an electroacupuncture (EA) plus needle retention (NR) group and an EA group. Both groups of patients will undergo the same routine cognitive rehabilitation treatments. All treatments will be given 5 times per week for 8 weeks. The primary outcomes will be assessed using the Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment scale (MOCA). The secondary outcome will be measured by the Barthel Index (BI). All outcomes will be evaluated at baseline, week 4, week 8, and the third and sixth month after the end of treatment. DISCUSSION: Our aim is to evaluate the effects of two different acupuncture treatment methods for treating PSCI patients. This study is expected to provide data to be used in developing an optimized acupuncture treatment method for PSCI treatment. TRIAL REGISTRATION: Chinese Clinical Trial Registry ChiCTR1900027849. Registered on 30 November 2019, http://www.chictr.org.cn/showproj.aspx?proj=46316.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.011 | 0.004 |
| 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) | 0.000 | 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 teacher head, 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".