Preeclampsia and the Risk of Bronchopulmonary Dysplasia in Preterm Infants Less Than 32 Weeks' Gestation
Bibliographic record
Abstract
Objective Angiogenesis is essential for normal lung development. The objective of our study was to test the hypothesis that preeclampsia, an antiangiogenic state, is a risk factor for bronchopulmonary dysplasia (BPD). Design Prospective cohort study of infants less than 32 weeks' gestation born to mothers with preeclampsia between January 2007 and June 2010 at a single tertiary care center. Their BPD outcome was compared with infants born to the next two normotensive mothers with a ± 1 week gestational age difference. BPD was defined as oxygen dependency at 36 weeks' postmenstrual age. Multivariable binary regression was used to estimate the risk ratio (RR) of BPD with preeclampsia exposure and adjust for confounders. Results Of 102 infants in the preeclampsia group, 23 (23%) developed BPD and of the 217 infants in the normotensive group, 56 (26%) developed BPD. On multivariable binary regression modeling, preeclampsia was not a risk factor for development of BPD (RR: 0.5, 95% confidence interval [CI]: 0.20–1.20). Surfactant use, Score for Neonatal Acute Physiology Perinatal Extension-II score, sepsis, blood transfusion, and intrauterine growth restriction (IUGR) were significant risk factors for BPD. Conclusion In our cohort, preeclampsia was not a significant risk factor for BPD. IUGR infants of preeclamptic and normotensive mothers were at higher risk for BPD.
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| 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".