Exposure to Maternal Diabetes during Pregnancy Is Associated with Aggravated Short-Term Neonatal and Neurological Outcomes following Perinatal Hypoxic–Ischemic Encephalopathy
Bibliographic record
Abstract
OBJECTIVE: Infants of diabetic mothers (IDM) are at higher risk of perinatal morbidities and glycemic instability, but the impact of maternal diabetes on neonatal and neurological short-term outcomes of neonates with hypoxic-ischemic encephalopathy (HIE) remains poorly described. Our objective was to determine the impact of maternal diabetes on neonatal and neurological short-term outcomes following neonatal HIE. STUDY DESIGN: This was a retrospective single-center study including 102 term neonates with HIE who received therapeutic hypothermia (TH) treatment between 2013 and 2020. Multiple regression analysis was used to assess the relationship between the presence of maternal diabetes and short-term outcomes. RESULTS: = 0.0432) compared with neonates of nondiabetic mother. Maternal diabetes was also associated with an increased risk of death or abnormal neurological examination at discharge in neonates with HIE (odds ratio: 6.41 [1.54-26.32]). CONCLUSION: In neonates with HIE, maternal diabetes is associated with an increased risk of death or short-term neonatal morbidities, such as longer duration of ventilation, prolonged neonatal stay, greater need for tube feeding, and being discharged with an abnormal neurological examination. Strategies to prevent, reduce, or better control maternal diabetes during pregnancy should be prioritized to minimize complications after perinatal asphyxia. KEY POINTS: · Maternal DB is associated with unfavorable outcomes.. · IDM have longer ventilatory support and tube feeding.. · IDM have higher risk of abnormal neurological examination..
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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 | 0.000 | 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".