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Record W2559918391

A study on risk factors for mild vascular cognitive impairment in patients with lacunar infarct

2016· article· en· W2559918391 on OpenAlexaboutno aff
Han Jiang, Qian Zeng, Song Chun-jiang, Bo Hsun Wu

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2016
Typearticle
Languageen
FieldNeuroscience
TopicNeurological Disease Mechanisms and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineLogistic regressionInternal medicineMontreal Cognitive AssessmentCognitive impairmentCognitionUnivariate analysisLeukoaraiosisCardiologyGastroenterologyMultivariate analysisDementiaPsychiatryDisease
DOInot available

Abstract

fetched live from OpenAlex

Objective To study the risk factors for vascular cognitive impairment (VCI) after lacunar infarct (LACI). Methods A total of 138 patients with LACI were evaluated by Montreal Cognitive Assessment (MoCA), and divided into normal cognitive function group (normal, N = 55), mild cognitive impairment (MCI) group (mild, N = 73) and severe cognitive impairment group (severe, N = 10). Univariate and backward multivariate Logistic regression analysis were used to screen the risk factors for VCI after LACI. Results Logistic regression analysis showed that infarct in critical site (OR = 1.179, 95% CI: 0.870-2.472; P = 0.012) and white matter hyperintensity (WMH) Grade 3-4 (OR = 2.005, 95% CI: 0.910-4.502; P = 0.024) were independent risk factors for VCI in patients with LACI. Conclusions VCI in patients with LACI is related to multiple factors, in which infarct in critical site and WMH Grade 3-4 are independent risk factors. DOI: 10.3969/j.issn.1672-6731.2016.11.010

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)0.001
metaresearch head score (Gemma)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.225
GPT teacher head0.497
Teacher spread0.271 · 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
Published2016
Admission routes1
Has abstractyes

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