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Record W2898458035 · doi:10.13702/j.1000-0607.170845

[Treatment of Vascular Cognitive Impairment by "Huayu Tongluo" Moxibustion].

2018· article· en· W2898458035 on OpenAlexaboutno aff
Hongliang Cheng, Fa-Cai Qian, Pei-Jia Hu, Wendong Zhang, Hengbin Yin, Fei Geng

Bibliographic record

VenuePubMed · 2018
Typearticle
Languageen
FieldNeuroscience
TopicNeurological Disease Mechanisms and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineMoxibustionMontreal Cognitive AssessmentTherapeutic effectInternal medicineTraditional Chinese medicineCognitive impairmentMini–Mental State ExaminationGastroenterologyAcupuncturePathology

Abstract

fetched live from OpenAlex

OBJECTIVE: To observe the therapeutic effect of "Huayu Tongluo"(blood stasis-removing and meridian-collateral-dredging) moxibustion for vascular cognitive impairment(VCI) patients and changes of insulin like growth factor -1(IGF-1) levels in serum after the treatment. METHODS: =30 in each group). Cotton cloth-separated moxibustion was applied to Baihui (GV 20) and Shenting (GV 24), and conventional moxibustion applied to Dazhui (GV 14) and Yongquan (KI 1) for 30 min, once daily, 6 times a week and for 30 days. Patients of the control group were treated by oral administration of Donepezil hydrochloride at the dose of 5 mg/night for 30 days. The core symptoms of traditional Chinese medicine (TCM), mini-mental state examination(MMSE), activity of daily living(ADL) and Montreal cognitive assessment(MoCA) scales were used to assess the therapeutic effect after the treatment. The content of serum IGF-1 was determined by ELISA. RESULTS: >0.05).. CONCLUSION: "Huayu Tongluo" moxibustion has a positive effect for patients with VCI, which may be associated with its effect in up-regulating serum IGF-1 level.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.033
GPT teacher head0.246
Teacher spread0.213 · 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 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
Published2018
Admission routes1
Has abstractyes

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