Intrapartum Characteristics and Prognosis Associated With Neonatal Hypoxic-Ischemic Encephalopathy [15F]
Bibliographic record
Abstract
INTRODUCTION: Neonatal hypoxic-ischemic encephalopathy (HIE) is associated with neonatal mortality, acute neurological injury and long-term neurodevelopmental disabilities; the role of intrapartum factors remains unclear. METHODS: This population-based cohort study employed linked obstetrical and newborn data derived from the Nova Scotia Atlee Perinatal Database (NSAPD, 1988-2015) and the Perinatal Follow-Up Program Database (2006-2015) for all pregnancies with live, non-anomalous newborns ≥35 weeks, born without pre-labor caesarean section. HIE was defined using standard definitions. Temporal trends in HIE incidence are described; Fisher's exact test and logistic regression were used to test the associations of intrapartum factors with HIE. RESULTS: The NSAPD identified 227 HIE cases out of a population of 226,711 pregnancies from 1988 to 2015, with a decrease in incidence from 0.14% to 0.1% through those years (P=0.01). Women with clinical chorioamnionitis in labor (OR 8.0, 95% CI 4.7-17), emergency operative delivery (OR 9.9, 95% CI 7.3-13), shoulder dystocia (OR 3.4, 95% CI 2.1-5.4), placental abruption (OR 19, 95% CI 12-29), and cord prolapse (OR 32, 95% CI 17-61) were more likely to have newborns with HIE. Two-thirds of newborns with HIE had an abnormal intrapartum FHR tracing. There was an infant mortality rate of 28% by age 3; neurodevelopmental outcomes in the surviving infants with HIE were normal in 33% and showed severe developmental delay in 37%. CONCLUSION: Overall, the rate of HIE was low in late-preterm and term infants. The identification of associated intrapartum factors should promote increased surveillance and careful management to optimize newborn outcomes.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".