Relationship between serum mineral levels in the second and third trimester of pregnancy and the risk of gestational diabetes mellitus: a retrospective cohort study
Bibliographic record
Abstract
Objective: To evaluate the relationship between serum mineral levels in pregnant women during the second and third trimesters and the risk of developing gestational diabetes mellitus (GDM). Design: Retrospective cohort study. Setting: A tertiary hospital in China. Population: Unselected women with singleton pregnancies who developed GDM. Methods: Maternal demographic data and serum mineral concentration information from the mid and late stages of pregnancy were collected through the hospital information system. Analyses were conducted using restricted cubic spline models and multivariate logistic regression models. Main outcome measures: The prevalence of GDM，specific serum mineral levels，gestational age at delivery. Results: Among 17 224 singleton pregnancies, the prevalence of GDM in this study was 15.07%. Chloride (P for overall = 0.01; P for nonlinear = 0.373; OR (95% CI) = 1.03 (1.01, 1.05)) showed a significant linear positive association with GDM. Additionally, serum levels of calcium (P for nonlinear < 0.001), potassium (P for nonlinear = 0.036), and magnesium (P for nonlinear < 0.001) were found to have nonlinear relationships with the risk of GDM. The interactions between calcium and magnesium (OR (95% CI) = 0.05 (0.01, 0.27), P for interaction < 0.001), potassium and magnesium (OR (95% CI) = 0.11 (0.03, 0.37), P for interaction < 0.001), and potassium and chloride (OR (95% CI) = 1.06 (1.01, 1.11), P for interaction < 0.001) were significant. Conclusions: The study indicates that specific serum mineral levels in pregnant women are closely associated with the risk of gestational diabetes mellitus. A deeper understanding of the mechanisms and interactions of these minerals could aid in developing effective prevention and treatment strategies, thereby reducing the incidence of GDM and improving pregnancy outcomes.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".