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Record W4365442782

Exercise Plus Acupuncture on Consensus Acupoints Versus Acupoints Selected by the Theory of Equal Impact on Tendons, Bones, and Muscles for Knee Osteoarthritis.

2023· article· en· W4365442782 on OpenAlexaboutno aff
Jiaxiang Yang, Xiangdong Lan, Qingcheng Cai, Ziheng Lu, Yanjun Wang

Bibliographic record

VenuePubMed · 2023
Typearticle
Languageen
FieldMedicine
TopicAcupuncture Treatment Research Studies
Canadian institutionsnot available
Fundersnot available
KeywordsAcupunctureMedicinePhysical therapyOsteoarthritisRandomized controlled trialMoxibustionVisual analogue scaleTraditional Chinese medicineClinical trialPhysical medicine and rehabilitationAlternative medicineSurgeryInternal medicine
DOInot available

Abstract

fetched live from OpenAlex

Context: Knee osteoarthritis (KOA) is a degenerative disorder that significantly affects patients' quality of life. Acupuncture and exercise are the most popular treatments currently. The outcomes for acupuncture for KOA, however, are controversial, with some researchers finding that the addition of acupuncture to exercise therapy provided no additional improvement in pain scores. Objective: The study intended to evaluate the therapeutic effects for KOA of exercise in combination with acupuncture on acupoints selected using the Traditional Chinse Medicine (TCM) theory of Equal Impact on Tendons, Bones, and Muscles (EITBM) in comparison with that of acupoints selected using classical consensus for the treatment. Design: The research team performed a randomized controlled trial. Setting: The study took place in the Department of Acupuncture and Moxibustion at the First Affiliated Hospital of Hebei University of Chinese Medicine in Shijiazhuang, Hebei, China. Participants: Participants were 70 patients with KOA who visited the hospital between December 2020 and February 2021. Intervention: Participants in both group received acupuncture plus exercise therapy. The research team randomly assigned participants to one of two groups: (1) 35 to the intervention group, which received acupuncture using acupoints selected using EITBM, and (2) 35 to the control group, which received acupuncture using the classical consensus acupoints. Both groups performed a 25-min session of acupuncture three times weekly for 4 weeks, with the exercise therapy following the acupuncture each time. Outcome Measures: The research team assessed clinical efficacy at baseline and postintervention. The primary outcome measures included assessments: (1) of knee joint pain using a visual analog scale (VAS), (2) of knee joint pain, flexibility, and function using the Western Ontario and McMaster University Osteoarthritis Index (WOMAC) pain subscale; and (3) joint range of motion (ROM). The secondary outcome measures included measurement of serum levels of interleukin-1 beta (IL-1ß), tumor necrosis factor alpha (TNF-α), and matrix metalloproteinase-13 (MMP-13) using enzyme-linked immunosorbent assays (ELISA). Results: The VAS and WOMAC scores significantly decreased for both groups between baseline and postintervention, and the intervention group's decrease was significantly greater than that of the control group. The ROM of knee flexion was significantly higher in both groups postintervention than at baseline, and the intervention group's increase was significantly higher than that of the control group. The serum IL-1ß, TNF-α, and MMP-13 also significantly decreased postintervention in both groups, and the intervention group's levels were significantly lower than those of the control group. The total effective rate was 94.1% in the intervention group, 32 out of 34 participants, and 75.8% in the control group, 25 out of 33 participants, which was significantly different. Conclusions: Acupuncture, in combination with exercise, can relieve symptoms, improve joint function, and reduce pro-inflammatory cytokines (IL-1ß and TNF-a) as well as MMP-13 for patients with KOA. The outcomes for acupuncture using EITBM acupoints were significantly better than those of the acupoints selected using classical consensus.

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)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.890
Threshold uncertainty score0.974

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.038
GPT teacher head0.302
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.

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

Citations2
Published2023
Admission routes1
Has abstractyes

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