Large‐for‐gestational‐age (LGA) neonate predicts a 2.5‐fold increased odds of neonatal hypoglycaemia in women with type 1 diabetes
Bibliographic record
Abstract
Abstract Objective The objective of the study is to assess the impact of maternal glycaemic control and large‐for‐gestational‐age (LGA) infant size on the risk of developing neonatal hypoglycaemia in offspring of women with type 1 diabetes and to determine possible predictors of neonatal hypoglycaemia and LGA. Research methods and design This retrospective cohort study evaluated pregnancies in 161 women with type 1 diabetes mellitus at a large urban centre between 2006 and 2010. Mean trimester A 1c values were categorized into five groups. Multiple logistic regression analyses were used to examine predictors of neonatal hypoglycaemia and large‐for‐gestational‐age (LGA). Results Hypoglycaemia occurred in 36.6% of neonates. There was not a linear association between trimester specific A 1c and LGA. After adjusting for maternal age, body mass index (BMI), smoking and premature delivery, neonatal hypoglycaemia was not linearly associated with A 1c in the first, second or third trimesters. LGA was the only significant predictor for neonatal hypoglycaemia (OR, 95% CI 2.51 [1.10, 5.70]) in logistic regression analysis that adjusted for glycaemic control, maternal age, smoking, prematurity and BMI. An elevated third trimester A 1c increased the odds of LGA (1.81 [1.03, 3.18]) after adjustment for smoking, parity and maternal BMI. Conclusions Large‐for‐gestational‐age imparts a 2.5‐fold increased odds of hypoglycaemia in neonates of women with type 1 diabetes and may be a better predictor of neonatal hypoglycaemia than maternal glycaemic control. Our data suggest that LGA neonates of women with type 1 diabetes should prompt increased surveillance for neonatal hypoglycaemia and that the presence of optimum maternal glycaemic control should not reduce this surveillance. Copyright © 2016 John Wiley & Sons, Ltd.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 | 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".