The Differential Impact of Delivery Hospital on the Outcomes of Premature Infants
Bibliographic record
Abstract
BACKGROUND: Because greater percentages of women deliver at hospitals without high-level NICUs, there is little information on the effect of delivery hospital on the outcomes of premature infants in the past 2 decades, or how these effects differ across states with different perinatal regionalization systems. METHODS: A retrospective population-based cohort study was constructed of all hospital-based deliveries in Pennsylvania and California between 1995 and 2005 and Missouri between 1995 and 2003 with a gestational age between 23 and 37 weeks (N = 1328132). The effect of delivery at a high-level NICU on in-hospital death and 5 complications of premature birth was calculated by using an instrumental variables approach to control for measured and unmeasured differences between hospitals. RESULTS: Infants who were delivered at a high-level NICU had significantly fewer in-hospital deaths in Pennsylvania (7.8 fewer deaths/1000 deliveries, 95% confidence interval [CI] 4.1-11.5), California (2.7 fewer deaths/1000 deliveries, 95% CI 0.9-4.5), and Missouri (12.6 fewer deaths/1000 deliveries, 95% CI 2.6-22.6). Deliveries at high-level NICUs had similar rates of most complications, with the exception of lower bronchopulmonary dysplasia rates at Missouri high-level NICUs (9.5 fewer cases/1000 deliveries, 95% CI 0.7-18.4) and higher infection rates at high-level NICUs in Pennsylvania and California. The association between delivery hospital, in-hospital mortality, and complications differed across the 3 states. CONCLUSIONS: There is benefit to neonatal outcomes when high-risk infants are delivered at high-level NICUs that is larger than previously reported, although the effects differ between states, which may be attributable to different methods of regionalization.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".