Observed Prevalence of Congenital Heart Defects From a Surveillance Study in China
Bibliographic record
Abstract
OBJECTIVES: The purpose of this study was to estimate the prevalence of major and minor congenital heart defects among fetuses and neonates using sonography in a general population of 4 areas surrounding Shanghai, China. METHODS: Pregnant women were recruited between April 2004 and December 2005 in Jiaxing City, Suzhou City, Changshu County, and Haining County. All participants could have 3 sonographic examinations performed by specially trained physicians regardless of medical indication: a fetal sonographic screen and fetal echocardiography between 20 and 28 weeks' gestation and neonatal echocardiography. Diagnoses of congenital heart defects were made on the basis of review of all available scans by an international group of experts in pediatric cardiology. Prevalence rates were calculated per 1000 births. RESULTS: Among 4006 scanned fetuses and neonates, there were 75 congenital heart defects, including 12 major defects. The observed prevalence for all congenital heart defects was 18.7 (95% confidence interval, 14.8-23.5) per 1000 births, and the prevalence for major defects was 3.0 (95% confidence interval, 1.6-5.2) per 1000 births. The most common defects were ventricular septal defects (n = 47 [62.7%]), atrial septal defects (n = 14 [18.7%]), tetralogy of Fallot (n = 4 [5.3%]), and hypoplastic left heart syndrome (n = 3 [4.0%]). CONCLUSIONS: The prevalence of all congenital heart defects in the 4 areas of China studied was higher than that reported in other countries, with ventricular septal defects being the most frequent defects. Our data likely reflect a better estimate of the total prevalence of congenital heart defects in China than reported previously.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".