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Record W4307055388 · doi:10.1093/pch/pxac100.008

9 Congenital Heart Disease and Autism Spectrum Disorders: Is There a Link?

2022· article· en· W4307055388 on OpenAlexaff
Sophia Gu, Qian Zhang, Abhay Katyal, Winnie Chung, Sonia Franciosi, Shubhayan Sanatani

Bibliographic record

VenuePaediatrics & Child Health · 2022
Typearticle
Languageen
FieldMedicine
TopicCongenital Heart Disease Studies
Canadian institutionsBC Children's Hospital
Fundersnot available
KeywordsMedicineTetralogy of FallotMeta-analysisGreat arteriesIncidence (geometry)Heart diseasePediatricsAutismCohortCohort studyProspective cohort studyInternal medicineCardiologyPsychiatry

Abstract

fetched live from OpenAlex

Abstract Background Congenital heart disease (CHD) has been linked to an increased incidence of intellectual disability and neurodevelopmental impairments in young patients. The relationship between CHD and autism spectrum disorders (ASD) is not well investigated. Objectives We performed a systematic review and meta-analysis of the medical literature to assess the evidence linking CHD to incidence of ASD. Design/Methods A systematic review of studies on CHD and ASD in PubMed, Cochrane and ISI from 1965 to May 2021 was conducted. Only retrospective or prospective cohort, case-control and cross-sectional human studies in the English language were included. Quantitative estimates of association between CHD and ASD were extracted from eligible studies for the meta-analysis. Pooled estimates were obtained using a random effect models fit by a generalised linear mixed model. Quantitative results not included in the meta-analysis were summarized using descriptive statistics. Results We screened 2,709 articles and 24 articles were included in this review. Among the 24 studies, there was a total of 348,771 subjects (12,114 CHD, 9,829 ASD and 326,828 controls). Patient age (range of 0 to 26 years) was reported in all studies. The proportion of males in CHD, ASD and controls were 50%, 80%, and 55%. CHD diagnosis ranged from non-specific (9/16), single ventricle lesion (2/16), dextro-transposition of the great arteries (2/16), and tetralogy of Fallot (1/16). Overall, seven articles reported a 4.66 times increase in the percentage of ASD cases in patients with CHD compared to the percentage of ASD cases in the general population: (5.87% vs 1.14% of ASD cases in CHD vs the general population). Seven of 24 studies were eligible for the meta-analysis, which included information on a total of 250,611 subjects (3,984 CHD, 9,829 ASD, and 236,798 controls). The summary estimate indicated that CHD patients are associated with almost double the odds of being diagnosed with autism compared with non-CHD patients: (OR 1.99, 95% CI 1.77-2.24, p<0.01). Early developmental delay, perinatal factors, genetics and neurological development were potential risk factors and etiologies for the onset of ASD in CHD patients. Suggestions for improvement of future clinical care include early identification and treatment of ASD among the CHD population. Limitations of this study include few articles explicitly exploring the link between CHD and ASD and lack of standardized ASD endpoints. Conclusion This review outlines that CHD patients are at an increased risk of presenting with a diagnosis or symptoms suggestive of ASD.

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)0.012
metaresearch head score (Gemma)0.041
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 score0.012
Threshold uncertainty score0.065

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.041
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0070.013
Bibliometrics0.0070.006
Science and technology studies0.0000.001
Scholarly communication0.0040.002
Open science0.0020.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.011
GPT teacher head0.268
Teacher spread0.257 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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