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Record W2314219463 · doi:10.1097/aap.0b013e31824f6629

Tai Chi and Chronic Pain

2012· review· en· W2314219463 on OpenAlexaff
Philip Peng

Bibliographic record

VenueRegional Anesthesia & Pain Medicine · 2012
Typereview
Languageen
FieldMedicine
TopicFibromyalgia and Chronic Fatigue Syndrome Research
Canadian institutionsUniversity Health Network
Fundersnot available
KeywordsMedicineFibromyalgiaOsteoarthritisPhysical therapyChronic painMeditationAlternative medicineRheumatoid arthritisQuality of life (healthcare)Intervention (counseling)Physical medicine and rehabilitationPsychiatryInternal medicineNursing

Abstract

fetched live from OpenAlex

In the last 2 decades, a growing body of research aimed at investigating the health benefits of Tai Chi in various chronic health conditions has been recognized in the literature. This article reviewed the history, the philosophy, and the evidence for the role of Tai Chi in a few selected chronic pain conditions. The ancient health art of Tai Chi contributes to chronic pain management in 3 major areas: adaptive exercise, mind-body interaction, and meditation. Trials examining the health benefit of Tai Chi in chronic pain conditions are mostly low quality. Only 5 pain conditions were reviewed: osteoarthritis, fibromyalgia, rheumatoid arthritis, low back pain, and headache. Of these, Tai Chi seems to be an effective intervention in osteoarthritis, low back pain, and fibromyalgia. The limitations of the Tai Chi study design and suggestions for the direction of future research are also discussed.

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)0.007
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.814
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.074
GPT teacher head0.343
Teacher spread0.269 · 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

Citations66
Published2012
Admission routes1
Has abstractyes

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