Surveillance of Congenital Heart Defects among Adolescents at Three U.S. Sites
Bibliographic record
Abstract
The prevalence, co-morbidities, and healthcare utilization in adolescents with congenital heart defects (CHDs) is not well understood. Adolescents (11 to 19 years old) with a healthcare encounter between January 1, 2008 (January 1, 2009 for MA) and December 31, 2010 with a CHD diagnosis code were identified from multiple administrative data sources compiled at 3 US sites: Emory University, Atlanta, Georgia (EU); Massachusetts Department of Public Health (MA); and New York State Department of Health (NY). The estimated prevalence for any CHD was 4.77 (EU), 17.29 (MA), and 4.22 (NY) and for severe CHDs was 1.34 (EU), 3.04 (MA), and 0.88 (NY) per 1,000 adolescents. Private or commercial insurance was the most common insurance type for EU and NY, and Medicaid for MA. Inpatient encounters were more frequent in severe CHDs. Cardiac co-morbidities included rhythm and conduction disorders at 20% (EU), 46% (MA), and 9% (NY) as well as heart failure at 3% (EU), 15% (MA), and 2% (NY). Leading noncardiac co-morbidities were respiratory/pulmonary (22% EU, 34% MA, 16% NY), infectious disease (17% EU, 22% MA, 20% NY), non-CHD birth defects (12% EU, 23% MA, 14% NY), gastrointestinal (10% EU, 28% MA, 13% NY), musculoskeletal (10% EU, 32% MA, 11% NY), and mental health (9% EU, 30% MA, 11% NY). In conclusion, this study used a novel approach of uniform CHD definition and variable selection across administrative data sources in 3 sites for the first population-based CHD surveillance of adolescents in the United States. High resource utilization and co-morbidities illustrate ongoing significant burden of disease in this vulnerable population.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".