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[Effect of <i>Cangguitanxue</i> acupuncture combined with suspension exercise therapy on chronic low back pain].

2020· article· en· W3041611088 on OpenAlexaboutno aff
Xu Wang, Jun-Song Zhu

Bibliographic record

VenuePubMed · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMyofascial pain diagnosis and treatment
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineAcupunctureVisual analogue scaleMcGill Pain QuestionnairePhysical therapyTrunkLow back painLumbarProprioceptionRandomized controlled trialSurgery

Abstract

fetched live from OpenAlex

OBJECTIVE: acupuncture combined with suspension exercise therapy on the clinical symptoms, lumbar proprioception and trunk isokinetic muscle strength in patients with chronic low back pain. METHODS: points, the acupuncture was given once a day, six times as a course of treatment, and a total of two courses of treatment were given. Before and after treatment, the scores of symptoms and signs, the pain rating index (PRI), present pain intensity (PPI) and the visual analogue scale (VAS) in the short-form of McGill pain questionnaire (SF-MPQ) in the two groups were recorded. The isokinetic feedback biomechanical test system was used to measure the lumbar proprioception and isokinetic muscle strength of the trunk, and the clinical efficacy of the two groups was evaluated. RESULTS: <0.05). CONCLUSION: acupuncture combined with suspension exercise therapy could effectively improve the symptoms and signs of patients with chronic low back pain, enhance the lumbar proprioception and trunk isokinetic muscle strength.

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 score0.003
Threshold uncertainty score0.011

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 integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.008
GPT teacher head0.199
Teacher spread0.192 · 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
Published2020
Admission routes1
Has abstractyes

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