Trends in Survival Among Children With Down Syndrome in 10 Regions of the United States
Bibliographic record
Abstract
OBJECTIVE: This study examined changes in survival among children with Down syndrome (DS) by race/ethnicity in 10 regions of the United States. A retrospective cohort study was conducted on 16,506 infants with DS delivered during 1983-2003 and identified by 10 US birth defects monitoring programs. Kaplan-Meier survival probabilities were estimated by select demographic and clinical characteristics. Adjusted hazard ratios (aHR) were estimated for maternal and infant characteristics by using Cox proportional hazard models. RESULTS: The overall 1-month and 1-, 5-, and 20-year survival probabilities were 98%, 93%, 91%, and 88%, respectively. Over the study period, neonatal survival did not improve appreciably, but survival at all other ages improved modestly. Infants of very low birth weight had 24 times the risk of dying in the neonatal period compared with infants of normal birth weight (aHR 23.8; 95% confidence interval [CI] 18.4-30.7). Presence of a heart defect increased the risk of death in the postneonatal period nearly fivefold (aHR 4.6; 95% CI 3.9-5.4) and continued to be one of the most significant predictors of mortality through to age 20. The postneonatal aHR among non-Hispanic blacks was 1.4 (95% CI 1.2-1.8) compared with non-Hispanic whites and remained elevated by age 10 (2.0; 95% CI 1.0-4.0). CONCLUSIONS: The survival of children born with DS has improved and racial disparities in infant survival have narrowed. However, compared with non-Hispanic white children, non-Hispanic black children have lower survival beyond infancy. Congenital heart defects are a significant risk factor for mortality through age twenty.
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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 | 0.000 | 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".