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Record W4361006187 · doi:10.3390/brainsci13040556

Incidence of Stress-Induced Hyperglycemia in Acute Ischemic Stroke: A Systematic Review and Meta-Analysis

2023· review· en· W4361006187 on OpenAlexaboutno aff
Haofuzi Zhang, Kangyi Yue, Zijian Jiang, Xiuquan Wu, Xin Li, Peng Luo, Xiaofan Jiang

Bibliographic record

VenueBrain Sciences · 2023
Typereview
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsCochrane LibraryIncidence (geometry)MedicineMeta-analysisInclusion and exclusion criteriaStroke (engine)Subgroup analysisInternal medicinePopulationWeb of sciencePathologyAlternative medicineEnvironmental health

Abstract

fetched live from OpenAlex

The aim of this study was to systematically evaluate the incidence of stress-induced hyperglycemia (SIH) in acute ischemic stroke (AIS). Studies that reported SIH incidence in AIS and examined risk factors for SIH and non-SIH patients were systematically searched in PubMed, Embase, Cochrane Library, and Web of Science from the inception of each database to December 2021. Article screening and data extraction were performed by two independent reviewers according to the inclusion and exclusion criteria. The quality of the included studies was assessed using the Newcastle-Ottawa Scale (NOS), and meta-analysis was performed using Stata. A total of 13 studies involving 4552 patients (977 in the SIH group and 3575 in the non-SIH group) were included. Meta-analysis showed that the incidence of SIH was 24% (95% CI: 21-27%) in the total population, 33% (14-52%) in North America, 25% (20-29%) in Europe, and 21% (12-29%) in Asia. Subgroup analysis by year of publication revealed that the pooled incidence of SIH was 27% (22-32%) in studies published before 2010 and 19% (14-24%) in those published after 2010. SIH is relatively common in AIS and poses a serious public health problem. Therefore, more emphasis should be placed on the prevention and control of SIH in AIS.

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.818
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0100.002
Bibliometrics0.0010.005
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.133
GPT teacher head0.403
Teacher spread0.270 · 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.

Study designSystematic review
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

Citations24
Published2023
Admission routes1
Has abstractyes

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