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Research on the value of methionine loading test in the mild vascular cognitive impairment

2015· article· en· W3032699553 on OpenAlexaboutno aff
Huaixiang Liu, Tan Xiao-mu, Jianguo Liu, Jing Zhang

Bibliographic record

VenueZhonghua laonian yixue zazhi · 2015
Typearticle
Languageen
FieldNeuroscience
TopicNeurological Disease Mechanisms and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsHyperhomocysteinemiaHomocysteineInternal medicineMedicineMontreal Cognitive AssessmentRisk factorGastroenterologyCognitive impairmentDisease

Abstract

fetched live from OpenAlex

Objective To study the value of methionine loading test (MLT) in the mild vascular cognitive impairment (VCI) after acute cerebral infarction. Methods The fasting plasma homocystine (Hcy) level and homocystine level after MLT were measured by high-performance liquid chromatography methods. We chose 240 patients with normal level of fasting plasma Hcy (normal group), 159 patients with normal level of Hcy after MLT, 81 patients with hyperhomocysteinemia after MLT (hyperhomocysteinemia group), and 112 patients with fasting hyperhomocysteinemia (fasting hyperhomocysteinemia group) in this study. The Montreal Cognitive Assessment (MoCA) and Mini-Mental State Examination (MMSE) were conducted in normal, hyperhomocysteinemia and fasting hyperhomocysteinemia groups on admission, at 7d, 14 d, 30 d after treatment. Results Logistic regression analysis showed that the increased level of Hcy might be an independent risk factor for VCI〔OR: 1.285, 95%CI: 1.038-1.265, P 0.05). There were no significant differences in MMSE and MoCA scores between patients with fasting hyperhomocysteinemia and patients with hyperhomocysteinemia after MLT on admission, 7 d, 14 d and 30 d after treatment (P>0.05). Conclusions Hcy may be an independent risk factor for VCI. The MLT can discover the dormant vascular risk factors for VCI, which offers a valuable detection method for early intervention and prevention in the clinical medicine. Key words: Brain infarction; Cystine; Cognition disorders

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)0.002
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.636
Threshold uncertainty score0.406

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.172
GPT teacher head0.374
Teacher spread0.202 · 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 designBench or experimental
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
Published2015
Admission routes1
Has abstractyes

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