Association of maternal mineral status with the risk of preterm birth: a retrospective cohort study
Bibliographic record
Abstract
Background: There has been a gradual increase in the proportion of preterm birth in China during the past several decades. Maternal malnutrition is a significant determinant for preterm birth. Nevertheless, comprehensive studies investigating serum mineral levels during pregnancy associated with preterm birth remain scarce. This study aims to assess the associations between maternal serum mineral levels and the risk of preterm birth. Methods: This retrospective cohort study of 18,048 pregnant women used data from a tertiary hospital in China from January 2016 to December 2022. Demographic data and serum mineral concentrations in the second and third trimesters of mothers were collected from the hospital information system. Analysis was performed using restricted cubic splines and logistic regression models. Results: The proportion of preterm birth in this study was 6.01%. Phosphorus [P for overall = 0.005; P for nonlinear = 0.490; OR (95%CI) = 1.11 (1.04, 1.18)] and chlorine [P for overall = 0.002; P for nonlinear = 0.058; OR (95%CI) = 1.11 (1.03, 1.19)] showed a significant positive correlation with preterm birth in a linear fashion. Furthermore, serum levels of potassium (P for nonlinear <0.001), sodium (P for nonlinear = 0.004), and magnesium (P for nonlinear <0.001) exhibited non-linear relationships with the risk of preterm birth. Conclusion: Serum levels of some minerals during pregnancy were associated with the risk of preterm birth among pregnant women. In addition to commonly recognized micronutrients such as folic acid, iron, and vitamin D, healthcare providers should also pay attention to the levels of these minerals during pregnancy.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".