Association between congenital heart disease and autism spectrum disorders: A protocol for a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Congenital heart disease (CHD), the most common heart defect in children, refers to congenital disease with abnormal development of the heart or large blood vessels during the fetal period. The researchers suggest that children with CHD show more obvious neurodevelopmental disorders than children with normal development, and children with CHD may have a higher risk of social interaction and communication disorders. This is similar to the characteristics of children with autism spectrum disorder (ASD). However, the association between type of CHD and ASD is not well understood. This systematic review and meta-analysis will reveal the relationship between type of CHD and ASD. METHODS: We will search the Cochrane Library, Embase, PubMed, China National Knowledge Infrastructure, Wanfang, Chinese Scientific Journals Full text, and China Biology Medicine disc databases using relevant subject terms and free words. We will use a fixed effects model or random effects model for meta-analysis. The risk of bias will be assessed by the Newcastle-Ottawa Scale and the agency for health care research and quality. Heterogeneity will be tested by Q statistics and I² values. Publication bias will be detected by funnel plots and Egger test. Subgroup analyses and sensitivity analyses will also be used to explore and interpret the heterogeneity. RESULTS: The study will afford additional insight into the investigation the association between type of CHD and ASD. CONCLUSIONS: The results will provide evidence for the early identification and early intervention of ASD in children with CHD, which may contribute to improving the neurodevelopmental outcome of children with CHD.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.063 | 0.104 |
| Meta-epidemiology (narrow) | 0.006 | 0.004 |
| Meta-epidemiology (broad) | 0.022 | 0.034 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.050 | 0.004 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".