MétaCan
Menu
← Back to cohort
Record W143760196

[Moxibustion on heat-sensitive acupoints for treatment of myofascial pain syndrome: a multi-central randomized controlled trial].

2008· article· en· W143760196 on OpenAlexaboutno aff
Rixin Chen, Ming-Fei Kang, Weili He, Shiyong Chen, Bo Zhang

Bibliographic record

VenuePubMed · 2008
Typearticle
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineMoxibustionVisual analogue scaleAcupunctureRandomized controlled trialMcGill Pain QuestionnaireHeat sensitiveTherapeutic effectMyofascial pain syndromeSchmidt sting pain indexPhysical therapyAnesthesiaSurgeryAlternative medicine
DOInot available

Abstract

fetched live from OpenAlex

OBJECTIVE: To observe the clinical therapeutic effect of moxibustion on heat-sensitive acupoints on myofascial pain syndrome (MPS). METHODS: Three-centre, single blind, randomized controlled trial method was adopted. One hundred and seven cases were randomly divided into an observation group (n = 57), and a control group (n = 50). The observation group were treated by moxibustion on the heat-sensitive acupoints and the control group by acupuncture, cupping and TDP. The therapeutic effects were assessed according to changes of the score of the short-form of McGill pain questionnaire composing of pain rating index (PRI), visual analogue scale (VAS) and present pain intensity (PPI) before and after treatment. RESULTS: The cured rate of 86.0% in the observation group was better than 24.0% in the control group. After treatment, the score of PRI, VAS, PPI indexes significantly improved in the observation group (P < 0.001), and were significantly better than those in the control group (P < 0.001). CONCLUSION: Moxibustion on the heat-sensitive acupoints has a high therapeutic effect on MPS.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.002
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0070.001

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.

Opus teacher head0.042
GPT teacher head0.255
Teacher spread0.213 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designRandomized trial
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

Citations18
Published2008
Admission routes1
Has abstractyes

Explore more

Same venuePubMed→French-language works237,207→