[Risk factors for congenital heart disease in Chinese neonates: a Meta analysis].
Bibliographic record
Abstract
OBJECTIVE: To investigate the major risk factors for congenital heart disease (CHD) in Chinese neonates and to provide a reference for the prevention of CHD. METHODS: A literature search was performed to collect the case-control studies on the risk factors for CHD in Chinese neonates published in 2001-2016. The relevant data were extracted accordingly. The quality of included studies was assessed by Newcastle-Ottawa Scale. Sensitivity analysis was conducted using different models to analyze the same data. The publication bias was assessed by Egger's test. RESULTS: A total of 17 case-control studies involving 2 930 cases and 4 952 controls were included. The Meta analysis showed that the major risk factors for CHD in Chinese neonates were as follows: mother with advanced age (OR=2.649, 95%CI: 1.675-4.189), cold or fever (OR=4.558, 95%CI: 2.901-7.162), medication use in early pregnancy (OR=3.961, 95%CI: 2.816-5.573), passive smoking (OR=2.766, 95%CI: 1.982-3.859), abnormal childbearing history (OR=2.992, 95%CI: 1.529-5.856), noise exposure (OR=3.030, 95%CI: 1.476-6.217), radiation exposure (OR=2.363, 95%CI: 1.212-4.607), decoration (OR=4.979, 95%CI: 3.240-7.653), gestational diabetes (OR=5.090, 95%CI: 3.132-8.274), and pet raising (OR=2.048, 95%CI: 1.385-3.029). CONCLUSIONS: Mothers with advanced age, cold or fever, medication use in early pregnancy, passive smoking, abnormal childbearing history, noise exposure, radiation exposure, decoration, gestational diabetes, and pet raising may increase the risk of CHD in Chinese neonates.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.036 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".