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[Effect of moxibustion on inflammatory factors and oxidative stress factors in patients with knee osteoarthritis: a randomized controlled trial].

2020· article· en· W3087007772 on OpenAlexaboutno aff

Bibliographic record

VenuePubMed · 2020
Typearticle
Languageen
FieldMedicine
TopicAcupuncture Treatment Research Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMoxibustionOsteoarthritisMedicineOxidative stressRandomized controlled trialPhysical therapyInternal medicineAlternative medicinePathology

Abstract

fetched live from OpenAlex

OBJECTIVE: To compare the clinical efficacy between moxibustion and acupuncture for knee osteoarthritis (KOA), and to observe the effect on serum tumor necrosis factor-α(TNF-α), interleukin-1β(IL-1β), superoxide dismutase (SOD) and malondialdehyde (MDA). METHODS: point on the affected side for 30 min.In the moxibustion group, moxibustion was adopted at knee for 60 min. The treatment was given once every two days for 4 weeks, totally 14 times. Before and after treatment, the western ontario and McMaster universities osteoarthritis index (WOMAC) score was compared, and the therapeutic effect was evaluated in the two groups. The contents of serum TNF-αand IL-1β, the activity of serum SOD and the serum level of MDA were detected in the two groups. RESULTS: <0.05). CONCLUSION: Moxibustion and acupuncture can relieve KOA symptoms, and the therapeutic effect of moxibustion is superior to acupuncture. The mechanism may be related to the reduction of serum inflammatory factor and oxidative stress factor.

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)0.002
metaresearch head score (Gemma)0.002
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 score0.008
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0080.001

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 head0.010
GPT teacher head0.235
Teacher spread0.225 · 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

Citations12
Published2020
Admission routes1
Has abstractyes

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