Acupuncture for mild cognitive impairment in elderly people
Bibliographic record
Abstract
BACKGROUND: Acupuncture has an unique role in preventing and managing mild cognitive impairment (MCI) in nonpharmaceutical therapies because of its small wound, mild pain, and high security for many years. However, there is no systematic review evaluating safety and efficacy of acupuncture for MCI in elderly people. Therefore, this study will provide a protocol to explore the effectiveness and safety of acupuncture for MCI in the elderly. METHODS: Retrieval from 8 electronic databases was conducted to determine eligible trials published until May, 2019. Homogeneity qualified studies were included for data were extracted such as study country location, demographic characteristics, and measure outcomes, and were analyzed by a random effect model and sensitivity analyses to identify heterogeneity. Review Manager (Revman Version 5.3) software will be used for data synthesis, sensitivity analysis, meta regression, subgroup analysis, and risk of bias assessment. A funnel plot will be developed to evaluate reporting bias. RESULTS: A total of 15 randomized control trials involving 1051 subjects were included. The results were as follows: Compared with the control group, the clinical efficacy rates of acupuncture was better, odds ratio = 2.52, 95% confidence interval (CI) (1.86, 3.42), P < .00001, mini-mental state examination scores (mean difference [MD] = 1.53, 95% CI [1.04, 2.01], P < .00001), Montreal cognitive assessment scores (MD = 2.05, 95% CI [1.17, 1.92], P < .00001), activity of daily living scale (MD = 1.71, 95% CI [-1.38, 4.79], P > .05), and clock drawing task scores (MD = 1.91, 95% CI [1.74, 2.08], P < .00001). CONCLUSION: This study shows that acupuncture is beneficial for improving aspects of cognitive function in elderly people with MCI, which suggests that acupuncture may be an effective alternative and complementary approach to existing therapies for elderly people. More rigorous experimental studies and longer follow-up studies should be conducted in the future.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".