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Record W2701801063

Meta-analysis on correlation between genetic polymorphism of ApoE and late onset Alzheimer's disease in Chinese population

2016· article· en· W2701801063 on OpenAlexaboutno aff
Shuling Liu, Ting Zhang, Wei Yue, Zhihong Shi, Yalin Guan, Shuai Liu, Xiaodan Wang, Yong Ji

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2016
Typearticle
Languageen
FieldMedicine
TopicAlzheimer's disease research and treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineApolipoprotein EDiseasePolymorphism (computer science)Early-onset Alzheimer's diseaseCorrelationGeneticsAge of onsetMeta-analysisAlzheimer's diseaseInternal medicineAlleleBiologyGene
DOInot available

Abstract

fetched live from OpenAlex

Objective To systematically review the correlation between genetic polymorphism of apolipoprotein E (ApoE) and late onset Alzheimer's disease (LOAD) in Chinese population. Methods Taking "ApoE, late onset Alzheimer's disease, polymorphism, China and Chinese" as retrieval words, databases of PubMed, EMBASE/SCOPUS, EBSCO-CINAHL, Cochrane Library, China Biology Medicine (CBM), China National Knowledge Infrastructure (CNKI) and Wanfang Data were retrieved with computer for collecting case-control studies about the correlation between genetic polymorphism of ApoE and LOAD in Chinese population in recent 20 years. Newcastle-Ottawa Scale (NOS) was used for methodological quality assessment. Meta-analysis was conducted by using RevMan 5.0 software. Results There were a total of 249 records through preliminary searching. After eliminating 113 duplicate ones and 124 articles which did not meet the inclusion criteria and adding one article by searching the references of 27 screened articles, 13 high-quality clinical trials were finally selected (NOS score ≥ 5). A total of 3372 subjects (1360 LOAD patients and 2012 controls) were included. Meta-analysis showed that the LOAD risk in population with allele ApoEε4 was significantly higher than those with allele ApoEε3 (OR = 3.710, 95%CI:2.960-4.640; P = 0.000), while had no statistical difference from those with allele ApoEε 2. Meta-analysis also showed that the LOAD risk in those with genotype ApoEε3/ε4 (OR = 3.160, 95%CI: 2.390-4.180; P = 0.000), genotype ApoE ε 2/ε 4 (OR = 3.410, 95% CI: 2.160-5.380; P = 0.000), genotype ApoE ε 4/ε 4 (OR = 16.400, 95% CI: 8.200-32.810; P = 0.000) was significantly higher than those with genotype ApoE ε 3/ε 3, while had no statistical differences from those with genotype ApoE ε 2/ε 3 and genotype ApoE ε 2/ε 2. Conclusions The evidences indicate that ApoEε4 allele and ApoE genotype ε3/ε4, ε2/ε4 and ε4/ε4 are high risk factors for LOAD in Chinese population. DOI: 10.3969/j.issn.1672-6731.2016.01.006

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)0.013
metaresearch head score (Gemma)0.031
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.016
Threshold uncertainty score0.070

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.031
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0160.035
Bibliometrics0.0060.007
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.294
GPT teacher head0.537
Teacher spread0.243 · 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 designMeta-analysis
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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