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Record W2998075589

The Effectiveness of Acupuncture on Sleep Disorders: A Narrative Review.

2020· article· en· W2998075589 on OpenAlexaboutno aff
Fung Kei Cheng

Bibliographic record

VenuePubMed · 2020
Typearticle
Languageen
FieldMedicine
TopicAcupuncture Treatment Research Studies
Canadian institutionsnot available
Fundersnot available
KeywordsAcupunctureMedicineMainstreamAlternative medicineQuality of life (healthcare)CredibilityHealth careFamily medicineTraditional medicineNursingPolitical science
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.836
Threshold uncertainty score0.450

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.022
GPT teacher head0.299
Teacher spread0.277 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations11
Published2020
Admission routes1
Has abstractyes

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