A simple model to predict risk of gestational diabetes mellitus from 8 to 20 weeks of gestation in Chinese women
Bibliographic record
Abstract
BACKGROUND: Gestational diabetes mellitus (GDM) is associated with adverse perinatal outcomes. Screening for GDM and applying adequate interventions may reduce the risk of adverse outcomes. However, the diagnosis of GDM depends largely on tests performed in late second trimester. The aim of the present study was to bulid a simple model to predict GDM in early pregnancy in Chinese women using biochemical markers and machine learning algorithm. METHODS: Data on a total of 4771 pregnant women in early gestation were used to fit the GDM risk-prediction model. Predictive maternal factors were selected through Bayesian adaptive sampling. Selected maternal factors were incorporated into a multivariate Bayesian logistic regression using Markov Chain Monte Carlo simulation. The area under receiver operating characteristic curve (AUC) was used to assess discrimination. RESULTS: The prevalence of GDM was 12.8%. From 8th to 20th week of gestation fasting plasma glucose (FPG) levels decreased slightly and triglyceride (TG) levels increased slightly. These levels were correlated with those of other lipid metabolites. The risk of GDM could be predicted with maternal age, prepregnancy body mass index (BMI), FPG and TG with a predictive accuracy of 0.64 and an AUC of 0.766 (95% CI 0.731, 0.801). CONCLUSIONS: This GDM prediction model is simple and potentially applicable in Chinese women. Further validation is necessary.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".