Congenital Heart Defects in the United States
Bibliographic record
Abstract
BACKGROUND: Because of advancements in care, there has been a decline in mortality from congenital heart defects (CHDs) over the past several decades. However, there are no current empirical data documenting the number of people living with CHDs in the United States. Our aim was to estimate the CHD prevalence across all age groups in the United States in the year 2010. METHODS: The age-, sex-, and severity-specific observed prevalence of CHDs in Québec, Canada, in the year 2010 was assumed to equal the CHD prevalence in the non-Hispanic white population in the United States in 2010. A race-ethnicity adjustment factor, reflecting differential survival between racial-ethnic groups through 5 years of age for individuals with a CHD and that in the general US population, was applied to the estimated non-Hispanic white rates to derive CHD prevalence estimates among US non-Hispanic blacks and Hispanics. Confidence intervals for the estimated CHD prevalence rates and case counts were derived from a combination of Taylor series approximations and Monte Carlo simulation. RESULTS: We estimated that ≈2.4 million people (1.4 million adults, 1 million children) were living with CHDs in the United States in 2010. Nearly 300 000 of these individuals had severe CHDs. CONCLUSIONS: Our estimates highlight the need for 2 important efforts: planning for health services delivery to meet the needs of the growing population of adults with CHD and the development of surveillance data across the life span to provide empirical estimates of the prevalence of CHD across all age groups in the United States.
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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 | 0.003 |
| 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) | 0.005 | 0.001 |
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".