Association between maternal plasma glucose levels during pregnancy and risk of preterm birth: a retrospective study
Bibliographic record
Abstract
BACKGROUND: Preterm birth is a leading cause of health problems and death in infants. This study aims to investigate the association between maternal plasma glucose levels during pregnancy and risk of preterm birth. METHODS: This population-based retrospective study of 6,842 pregnant women used data from a tertiary hospital in China from January 2016 to December 2022. Plasma glucose levels were measured at fasting, 1 h, and 2 h after a 75-g OGTT between 24 and 28 weeks of gestation. The primary outcome of interest was preterm birth. Analysis was performed using restricted cubic splines and logistic regression models. RESULTS: The proportion of gestational diabetes mellitus (GDM) and preterm birth in this study were 7.92% and 5.86%, respectively. The levels of fasting plasma glucose (aOR: 1.26; 95% CI: 1.08, 1.47; P = 0.003; P for nonlinear = 0.264), 1-hour plasma glucose (aOR: 1.10; 95% CI: 1.03, 1.17; P = 0.003; P for nonlinear = 0.535), and 2-hour plasma glucose (aOR: 1.10; 95% CI: 1.02, 1.19; P = 0.012; P for nonlinear = 0.368) showed statistically significant linear associations with an increased risk of preterm birth. CONCLUSION: Elevated plasma glucose levels during pregnancy statistically significantly increase the risk of preterm birth. Given that hyperglycemia during pregnancy can be prevented and managed, it is crucial to enhance health education and glucose monitoring for pregnant women. Timely interventions should be implemented to control plasma glucose levels, thereby reducing the incidence of preterm birth.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.001 | 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".