A Systematic Review and Evaluation of Non-Pharmacological Interventions for Elderly Patients with Mild Cognitive Impairment.
Bibliographic record
Abstract
Objective: This study aims to comprehensively summarize and evaluate the impact of non-pharmacological interventions on mild cognitive impairment (MCI) in elderly individuals through a systematic review of pertinent literature. The interventions include acupuncture, massage, ear point pressing, acupoint moxibustion, dietary modifications, and exercise interventions. Methods: A thorough literature search spanned 2017 to 2023 across databases like Zhichou, Wanfang, PubMed, CINAHL, Web of Science, and the Cochrane Library. It covered pharmacological and non-pharmacological interventions, emphasising MCI in elderly patients. Independent screening, evaluation, and data extraction were conducted and assessed via the AMSTAR 2 scale and GRADE approach. Outcome measures (e.g., MMSE, MoCA, ADL, CDT, overall efficacy) were analyzed. Results: Three systematic evaluations were assessed using AMSTAR 2. Two were low quality, one moderate. Limited rigor in two studies led to considering only medium-quality papers for evidence grading. Key indicators in RCTs included MMSE (eight studies), MoCA (seven studies), ADL (two studies), CDT (two studies), and overall efficacy (12 studies). GRADE evaluation revealed moderate, high, and high evidence quality for intervention efficacy at one, two, and three months respectively. MMSE evidence was low, MoCA high, ADL very low, and CDT moderate. Adverse events were reported in one publication, suggesting acupuncture's potential pain and resistance. Conclusions: Non-pharmacological interventions, like acupuncture, cognitive exercises, and exercise, show promise in mild cognitive impairment among the elderly. They enhance cognitive function and daily living while maintaining safety. Acupuncture notably improves MoCA scores, supported by robust evidence.
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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 | 0.019 | 0.068 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.010 |
| Bibliometrics | 0.015 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".