A Study of Clinical Research Acupuncture Treatment on Post-stroke Cognitive Disorder
Bibliographic record
Abstract
Objective: The aim of this study was to investigate acupuncture therapy for post-stroke cognitive disorder to suggest the methods of a study about acupuncture therapy.Methods: Several academic databases were used in this study, including National Digital Science Library (NDSL), National Assembly Library of Korea, Korean Traditional Knowledge Portal, Research Information Sharing Service (RISS), Korean studies Information Service System (KISS), Korean Medical Database (KMBASE), Korea Med, Oriental Medicine Advanced Searching Integrated System (OASIS), PubMed, MEDLINE with Full Text, and China National Knowledge Infrastructure (CNKI).Keywords used were "중풍", "뇌졸중", "인지장애", "침", "acupuncture", and "cognitive disorder".Results: Ultimately, 28 papers were investigated.that were mainly published in 2012 and 2016.Of these, More than two-thirds received a score of one or less on the Jadad scale.Acupuncture points such as GV20, GV24, PC6, EX-HN1, SP6, and GV26 were most frequently used.The treatment duration was mostly 30 minutes or less, and the treatment cycle was mostly for four weeks.Dilatational wave was generally used in pulse wave form, and amplitude of electricity was increased until patients were able to endure.G6805 was generally used in electric acupuncture apparatus.According to results of acupuncture treatment, assessments such as the MMSE (Mini Mental State Examination) and the Montreal Cognitive Function Assessment Scale (MoCA) showed statistically significant improvements in 28 studies.Conclusions: Future research is needed to standardize the treatment of acupuncture, and more diversified high quality papers should be published to help clarify the therapeutic effects of acupuncture and the mechanisms of cognitive disorder post-stroke.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".