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Record W4214945161 · doi:10.37766/inplasy2022.3.0012

The efficacy and safety of warming acupuncture and moxibustion combined with Chinese herb medicine on knee osteoarthritis A protocol for a systematic review and meta-analysis

2022· review· en· W4214945161 on OpenAlexaboutno aff
Shijuan Tu, Yali Zheng, Ni Jin, Haijun Yang

Bibliographic record

Venuenot available
Typereview
Languageen
FieldMedicine
TopicHealthcare and Venom Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineOsteoarthritisMoxibustionAcupunctureWOMACPhysical therapyQuality of life (healthcare)Traditional Chinese medicineJoint painRandomized controlled trialClinical trialVisual analogue scaleTraditional medicineAlternative medicineInternal medicine

Abstract

fetched live from OpenAlex

Review question / Objective: P: Patients with knee osteoarthritis; I: The treatment group includes warming acupuncture and moxibustion combination with Chinese herb medicine at least; C: while the control group is the comparison of western medicine or other physical therapy.;O: overall response rate, pain score (such as the visual analogue scale [VAS]); joint function score (such as Lysholm score, Western Ontario and McMaster Universities Arthritis Index [WOMAC], and other scoring systems), daily life quality score (such as SF-36 score [the MOS item short from health survey]), and adverse reactions.;S: Randomized controlled trial.Condition being studied: Knee osteoarthritis has become a major public health problem, It could cause knee joint pain, joint instability and dysfunction, and seriously affects the quality of life of patients, The purpose of this systematic review is to evaluate the better efficacy and safety of warming acupuncture and moxibustion combined with Chinese herb medicine in the treatment of knee osteoarthritis (KOA) so as to provide comprehensive evidence for the selection of the optimal physical therapy regimens in the clinical treatment ofKOA.

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.739
Threshold uncertainty score0.714

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0090.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.139
GPT teacher head0.465
Teacher spread0.326 · 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 designSystematic review
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

Citations0
Published2022
Admission routes1
Has abstractyes

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