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[Efficacy of thunder-fire moxibustion combined with external applicaion of <i>Shuangbai</i> powder for mild and moderate knee osteoarthritis].

2019· article· en· W3024071828 on OpenAlexaboutno aff
Hu Qu, Zhen Zeng, Guang-Yun Hu, Xue-Mei Dai, Ying-Xuan Gu, Yang-Yue Zhang, Xiao-Ming Quan

Bibliographic record

VenuePubMed · 2019
Typearticle
Languageen
FieldMedicine
TopicOsteoarthritis Treatment and Mechanisms
Canadian institutionsnot available
Fundersnot available
KeywordsMoxibustionMedicineOsteoarthritisThunderWOMACVisual analogue scalePhysical therapySurgeryAcupuncture

Abstract

fetched live from OpenAlex

OBJECTIVE: powder and thunder-fire moxibustion alone for mild and moderate knee osteoarthritis. METHODS: powder was given to the affected knee after the treatment of thunder-fire moxibustion. Simple thunder-fire moxibustion was given in the control group. All patients in the two groups were treated once a day, 7 days as one course and the consecutive 4 courses were required, with an interval of 1 day between courses. Before and after treatment, the visual analogue scale (VAS) score and Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) score were used to assessed knee pain, stiffness and physical function in the two groups. In addition, the efficacy was evaluated. RESULTS: <0.05). CONCLUSION: powder are superior to simple thunder-fire moxibustion in improving the symptoms and delaying the development of the disease for mild and moderate knee osteoarthritis.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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)0.0020.000

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 head0.014
GPT teacher head0.217
Teacher spread0.203 · 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 designNon-randomized 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

Citations3
Published2019
Admission routes1
Has abstractyes

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