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Record W4392733987 · doi:10.1097/hm9.0000000000000097

Clinical research on Tai Chi: a review of health benefits

2024· review· en· W4392733987 on OpenAlexaff
Yuke Teng, Weiming Luo, Jun Zhou, Muchen Wang, Jinghan Wu, Xiaoqian Nie, Cong Sun, Ziyan Liang, Baiyang Liu, Furong Zhang, Tianyu Liu, Zhao Shi, Fanrong Liang, Xiaofan Yang, Zhengjie Li

Bibliographic record

VenueAcupuncture and Herbal Medicine · 2024
Typereview
Languageen
FieldHealth Professions
TopicBalance, Gait, and Falls Prevention
Canadian institutionsCAE (Canada)
Fundersnot available
KeywordsPsychologyMedicine

Abstract

fetched live from OpenAlex

Tai Chi, a traditional Chinese martial art and health exercise, has gained significant global attention and recognition. The clinical benefits of Tai Chi have been studied extensively in recent decades. We conducted a systematic search of PubMed, Web of Science, EMBASE databases, and the China National Knowledge Infrastructure, identifying and reviewing 241 clinical trials on the benefits of Tai Chi from their inception to June 1, 2023. Most of these studies have consistently demonstrated the positive impact of Tai Chi on balance, cognitive function, mood regulation, sleep quality, pain relief, cardiorespiratory function, and overall health. In the future, the key to fostering the ongoing development of this field lies in comprehensive research on standardized study designs and reporting, the scope of Tai Chi applications, and the core factors influencing its efficacy.

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)0.013
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.457
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0130.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0060.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.005
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 head0.350
GPT teacher head0.614
Teacher spread0.265 · 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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations8
Published2024
Admission routes1
Has abstractyes

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