Trends in Preterm Birth and Perinatal Mortality Among Singletons: United States, 1989 Through 2000
Bibliographic record
Abstract
OBJECTIVE: Despite the recent increase in preterm birth in the United States, trends in preterm birth subtypes have not been adequately examined. We examined trends in preterm birth among singletons following ruptured membranes, medical indications, and spontaneous preterm birth and evaluated the impact of these trends on perinatal mortality. METHODS: A population-based, retrospective cohort study comprising 46,375,578 women (16% blacks) who delivered singleton births in the United States, 1989 through 2000, was performed. Rates of preterm birth (< 37 weeks), their subtypes, and associated perinatal mortality (stillbirths at >/= 22 weeks plus neonatal deaths within 28 days), before and after adjustment for potential confounders, were derived from ecological logistic regression models. RESULTS: Preterm birth rates increased by 14% (95% confidence interval 13-15%) among whites from 8.3% to 9.4% and decreased by 15% (95% confidence interval 14-16%) among blacks from 18.5% to 16.2% between 1989 and 2000. Among whites, preterm birth following ruptured membranes declined by 23%, medically indicated preterm birth increased by 55%, and spontaneous preterm birth increased by 3%. Among blacks, preterm birth following ruptured membranes declined by 37%, medically indicated preterm birth increased by 32%, and spontaneous preterm birth decreased by 27%. The largest decline in perinatal mortality among whites was associated with increases in medically indicated preterm birth, whereas the largest decline in perinatal mortality among blacks was associated with declines in preterm birth following ruptured membranes and spontaneous preterm birth. CONCLUSION: Temporal trends in preterm birth varied substantially based on underlying subtype and maternal race. The recent increase in medically indicated preterm birth was associated with a favorable reduction in perinatal mortality.
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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 | 0.001 | 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".