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Record W2355177061

Efficacy Observation on Acupuncture with Diverse Frequencies in Treating Mild Vascular Cognitive Impairment

2013· article· en· W2355177061 on OpenAlexaboutno aff
Hu Hu

Bibliographic record

VenueShanghai zhenjiu zazhi · 2013
Typearticle
Languageen
FieldNeuroscience
TopicNeurological Disease Mechanisms and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsAcupunctureMedicineTreatment and control groupsSignificant differenceMontreal Cognitive AssessmentClinical efficacyCognitive impairmentCognitionTherapeutic effectInternal medicinePsychiatryAlternative medicine
DOInot available

Abstract

fetched live from OpenAlex

Objective To observe the clinical efficacy on acupuncture of diverse frequencies in treating mild vascular cognitive impairment(MVCI).Method Thirty-two patients with MVCI were divided into a treatment group of 16 cases and a control group of 16 cases according to patients' own wills.The two groups both received acupuncture treatment,3 times a week in the treatment group and 2 times a week in the control group.Clinical efficacies and changes of Montreal Cognitive Assessment(MoCA) score were compared after 10-week treatment.Result The two groups both had marked changes in MoCA score after treatment(P 0.01).The total effective rate was 64.3%in the treatment group versus 50.0%in the control group,and the difference was statistically insignificant(P0.05).In comparing MoCA score after treatment,the difference between the treatment group and the control group was statistically insignificant(P0.05).Both groups had marked changes in terms of attention and calculation,language,delayed memory,and visuospatial abilities in MoCA after treatment(P0.01).Conclusion Acupuncture intervention has accurate short-term therapeutic efficacy for MVCI,and the two treatment frequencies,twice a week and three times a week,don't make significant difference in treatment effect.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.546
Threshold uncertainty score0.816

Codex and Gemma teacher scores by category

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

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 head0.046
GPT teacher head0.259
Teacher spread0.212 · 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

Citations0
Published2013
Admission routes1
Has abstractyes

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