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Clinical trial of nimodipine combine with butylphthalide in the treatment of patients with mild to moderate vascular cognitive impairment

2019· article· en· W3029001078 on OpenAlexaboutno aff
Gezhi Zhou, Fan Yang

Bibliographic record

VenueZhongguo jiceng yiyao · 2019
Typearticle
Languageen
FieldNeuroscience
TopicNeurological Disease Mechanisms and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsNimodipineMedicineCognitive impairmentMontreal Cognitive AssessmentClinical efficacyInternal medicineAdverse effectCapsuleClinical trialAnesthesia

Abstract

fetched live from OpenAlex

Objective To evaluate the clinical efficacy and safety of nimodipine combine with butylphthalide in the treatment of patients with mild to moderate vascular cognitive impairment(VCI). Methods From January 2012 to December 2016, 100 patients with mild to moderate VCI in Jinhua Municipal Central Hospital were randomly divided into control group(n=50) and treatment group(n=50) according to the random number table method.The control group received butylphthalide capsule, 200 mg po tid.The treatment group received nimodipine tablets, 40mg po tid, on the basis of the control group.The two groups of patients were treated for 24 weeks.Montreal cognitive assessment(MoCA), activities of daily living(ADL), serum hs-CRP, IL-6, TNF-α, clinical efficacy and adverse drug reactions were compared after treatment. Results After treatment, the scores of MoCA and ADL in the treatment group were (24.32±2.87)points, (59.22±6.17)points, respectively, which were significantly higher than those in the control group[(22.76±2.67)points, (55.63±6.3)points, t=2.814, 2.870, all P 0.05). Conclusion Nimodipine combined with butylphthalide in the treatment of mild to moderate VCI is effective and has high safety. Key words: Cognition disorders, vasculogenic; Drug therapy, combination; C-reaction protein; Interleukins 6; Tumor necrosis factorα; Comparative effectiveness research; Nimodipine; Butylphthalide

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.127
Threshold uncertainty score0.542

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.044
GPT teacher head0.305
Teacher spread0.261 · 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
Published2019
Admission routes1
Has abstractyes

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