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Record W4411407214 · doi:10.1016/j.jacadv.2025.101898

Incidence of Stroke in Adults With Congenital Heart Disease

2025· article· en· W4411407214 on OpenAlexaboutno aff
Amanda Bilski, Jonathan Kochav, Greer Waldrop, Gular Mammadli, Mehriban Sariyeva, Melissa Argenio, Joshua Z. Willey, Chinwe Ibeh, Marlon Rosenbaum, Matthew A. Lewis, Eliza C. Miller

Bibliographic record

VenueJACC Advances · 2025
Typearticle
Languageen
FieldMedicine
TopicCongenital Heart Disease Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineStroke (engine)Incidence (geometry)Meta-analysisHeart diseasePopulationInternal medicinePediatrics

Abstract

fetched live from OpenAlex

BACKGROUND: Congenital heart disease is associated with an increased risk of cerebrovascular events. OBJECTIVES: The authors investigated the incidence of stroke and transient ischemic attack (TIA) in adults with congenital heart disease (ACHD). METHODS: A systematic review was performed per Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to identify all studies reporting the incidence of stroke and TIA in ACHD. Two independent reviewers screened studies, which were included if patients were of age 16 years or older with congenital heart disease and if the outcome was stroke or TIA. Random-effects meta-analysis was conducted to estimate the pooled incidence rate of stroke and TIA with 95% CIs. The Newcastle-Ottawa Scale for Risk of Bias was applied. This systematic review is registered (CRD42022322144). RESULTS: : 98%; P = 0.01). CONCLUSIONS: This meta-analysis describes the incidence rate of stroke and TIA in the ACHD population. High-quality studies are needed to identify which ACHD patients are at the highest risk and to develop effective strategies for primary stroke prevention in this population.

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)0.009
metaresearch head score (Gemma)0.029
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.029
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.013
Bibliometrics0.0050.004
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0010.001
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 head0.006
GPT teacher head0.288
Teacher spread0.281 · 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

Citations3
Published2025
Admission routes1
Has abstractyes

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