Effect of Opening Midlevel Neonatal Intensive Care Units on the Location of Low Birth Weight Births in California
Bibliographic record
Abstract
OBJECTIVE: Despite evidence and recommendations encouraging the delivery of high-risk newborns in hospitals with subspecialty or high-level NICUs, increasing numbers are being delivered in other facilities. Causes for this are unknown. We sought to explore the impact of diffusion of specialty or midlevel NICUs on the types of hospitals in which low birth weight newborns are born. DESIGN: We used birth certificate, death certificate, and hospital discharge data for essentially all low birth weight, singleton California newborns born between 1993 and 2000. We identified areas likely to have been affected by the opening of a new nearby midlevel unit, analyzed changes over time in the share of births that took place in midlevel NICU hospitals, and compared patterns in areas that were and were not likely affected by the opening of a new midlevel unit. We also tracked the corresponding changes in the share of births in high-level hospitals and in those without NICU facilities (low-level). RESULTS: The probability of a 500- to 1499-g infant being born in a midlevel unit increased by 17 percentage points after the opening of a new nearby unit. More than three quarters of this increase was accounted for by reductions in the probability of birth in a hospital with a high-level unit (-15 points), and the other portion was resulting from reductions in the share of newborns delivered in hospitals with low-level centers (-2 points). Similar patterns were observed in 1500- to 2499-g newborns. CONCLUSIONS: The introduction of new midlevel units was associated with significant shifts of births from both high-level and low-level hospitals to midlevel hospitals. In areas in which new midlevel units opened, the majority of the increase in midlevel deliveries was attributable to shifts from high-level unit births. Continued proliferation of midlevel units should be carefully assessed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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) | 0.000 | 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 teacher head, 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".