The Effectiveness of Acupuncture on Sleep Disorders: A Narrative Review.
Bibliographic record
Abstract
BACKGROUND: Sleep disorders erode quality of life and increase public medical expenditure. Aside from mainstream therapies, complementary and alternative medicines have been widely adopted, among which the use of acupuncture is rising. PRIMARY STUDY OBJECTIVE: This narrative review analyses research outcomes, and then provides an overview of the effects of acupuncture on sleep problems caused by various factors. METHOD: This research reviews 79 empirical projects with 6589 participants in mainland China, Hong Kong, Taiwan, Japan, Korea, Germany, Iran, Brazil, Canada, and the United States, aged 15to 85 years, which are retrieved from 29 promising electronic databases in English and Chinese. RESULTS: The findings support the usefulness of this method to deal with sleep disturbances through different forms of acupuncture, including traditional, sham, shock wave, venom, and Mongolian modes, as well as acupressure, either applied standalone or coupled with other therapies. CONCLUSION: This review sheds light on the modernisation of acupuncture to expand medical options for both health care professionals and patients with sleep difficulties. Despite this, improvements in research design are suggested to attain better credibility in order to substantiate the curative, remedial, rehabilitative, and preventive treatments of acupuncture to enhance sleep quality.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 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".