Extremely Preterm Infant Mortality Rates and Cesarean Deliveries in the United States
Bibliographic record
Abstract
OBJECTIVES: To estimate trends in infant mortality rates and cesarean delivery rates for extremely preterm infants born in the United States. METHODS: This national population-based study used public data from the Centers for Disease Control and Prevention to investigate extremely preterm infants born alive between 22 0/7 and 27 6/7 weeks of gestational age from 1999 to 2005. RESULTS: There were 177,552 extremely preterm infant births (fewer than 1% of all births) from 1999 to 2005. The number of annual extremely preterm births increased by 7% compared with a 4.5% increase for births at all gestations. During the study years, the extremely preterm infant mortality rate (percentage of infants who died in the first year) remained steady (range 33-34%; P=.22), whereas the cesarean delivery rate increased from 43% to 54% (P<.001). The infant mortality rate after cesarean delivery increased from 24% to 26% (P=.012). At each gestational age, the annual cesarean delivery rate increased over time (P<.001 for each), whereas gestational age-specific infant mortality rates were unchanged except for a 2% decline from 2004 to 2005 for infants born at 24 weeks of gestation (P=.01). CONCLUSION: A significant rise in the cesarean delivery rate in the United States from 1999 to 2005 for infants born at less than 28 weeks of gestation was not associated with an improvement in the infant mortality rate.
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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 | 0.002 |
| 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".