Impact of Maternal Diabetes Mellitus on Neonatal Outcomes among Infants <32 Weeks of Gestation in China: A Multicenter Cohort Study
Bibliographic record
Abstract
OBJECTIVE: Our study aimed to determine the relationship between maternal diabetes mellitus (MDM) and mortality and major morbidities for very preterm infants, as well as the effects of insulin-treated MDM, in the Chinese population. STUDY DESIGN: weeks of gestation and admitted to 57 tertiary neonatal intensive care units participating in the Chinese Neonatal Network in 2019. All infants were followed up until discharging from the hospitals. RESULTS: A total of 9,244 very preterm infants were enrolled, with 1,584 (17.1%) born to mothers with MDM. The rates of mortality or any major morbidity in the MDM and non-MDM groups were 45.9% (727/1,584) and 48.1% (3,682/7,660), respectively. After adjustment, the risk of mortality or any morbidity was not significantly increased in the MDM group (adjusted odds ratio [aOR], 1.07; 95% confidence interval [CI], 0.94-1.22) compared with the non-MDM group. Among MDM mothers with treatment data, 18.0% (256/1,420) were treated with insulin. Insulin-treated MDM was not independently associated with the risk of mortality or any morbidity (aOR, 1.01; 95% CI, 0.76-1.34) among very preterm infants, but it was associated with an elevated risk of severe retinopathy of prematurity (aOR, 2.39; 95% CI, 1.13-5.04). CONCLUSION: While the MDM diagnostic rate for mothers of very preterm infants was high in China, MDM was not associated with mortality or major morbidities for very preterm infants. KEY POINTS: · A total of 17% of very preterm infants in Chinese neonatal intensive care units were born to mothers with MDM.. · MDM was not related to mortality or major morbidities in very preterm infants.. · MDM treated by insulin was associated with severe retinopathy of prematurity..
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".