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Record W3158947472 · doi:10.1177/00033197211010096

Decreased Flow-Mediated Dilatation in Children With Type 1 Diabetes: A Systematic Review and Meta-Analysis

2021· review· en· W3158947472 on OpenAlexaboutno aff
Lei Cao, Miao Hou, Wanping Zhou, Ling Sun, Jie Shen, Yunjia Tang, Bo Wang, Xuan Li, Haitao Lv

Bibliographic record

VenueAngiology · 2021
Typereview
Languageen
FieldMedicine
TopicCardiovascular Health and Disease Prevention
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsMedicineMeta-analysisType 1 diabetesCochrane LibraryInternal medicineDiseaseDiabetes mellitusCardiologyEndocrinology

Abstract

fetched live from OpenAlex

Type 1 diabetes (T1DM) is a strong risk factor for the development of cardiovascular disease. Flow-mediated dilatation (FMD) is an early noninvasive marker of endothelial function and it predicts future cardiovascular disease. However, the changes in FMD among T1DM children are still controversial. The present meta-analysis aimed to investigate whether FMD is impaired in children with T1DM. PubMed, EMBASE, Cochrane library, and Web of Science were searched for studies comparing FMD in children with T1DM and healthy controls. The Newcastle-Ottawa quality assessment scale for case–control studies was used to assess study quality. Data were pooled using a random effects models to obtain the weighted mean differences (WMD) in FMD and 95% CIs. Overall, 19 studies with 1245 patients and 872 healthy controls were included in this meta-analysis. Children with T1DM had significantly lower FMDs compared with healthy controls (WMD: −2.58; 95% CI: −3.36 to −1.81; P < .001). Meta-regression analysis revealed that low-density lipoprotein cholesterol levels impacted the observed difference in FMD between T1DM and healthy children. This meta-analysis showed that T1DM children have impaired endothelial function, which indicates they are at higher risk of developing cardiovascular disease in later life.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.686
Threshold uncertainty score0.718

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0080.002
Bibliometrics0.0000.001
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 head0.042
GPT teacher head0.338
Teacher spread0.296 · 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 designMeta-analysis
Domainnot available
GenreReview

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

Citations14
Published2021
Admission routes1
Has abstractyes

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