Serum Ferritin Combined with Glycated Hemoglobin for Early Prediction of Gestational Diabetes Mellitus: A Retrospective Cohort Study
Bibliographic record
Abstract
Objective To investigate the value of serum ferritin (SF) in conjunction with glycated hemoglobin (HbA1c) for the early prediction of gestational diabetes mellitus (GDM) and to provide insights that could enhance health care standards for women and newborns. Methods A retrospective cohort study was conducted involving 650 pregnant women who received regular prenatal check-ups at our institution from January 2019 to April 2024. Participants were categorized into four groups based on their SF concentration quartiles during the 11th to 13th weeks of gestation. Logistic regression analyses were conducted to assess the predictive value of early GDM risk factors, with the lowest quartile group serving as a reference. Results The incidence rate of GDM rose progressively with increasing SF concentrations at 11–13 weeks of gestation, with rates of 18.79%, 21.25%, 24.38%, and 25.45% respectively. Notably, the incidence rate in the highest quartile group (quartile 4) was significantly higher compared to the lowest (quartile 1), with an odds ratio of 1.48 and a 95% confidence interval of 1.12 to 1.93. Additionally, the predictive model incorporating both SF concentration and HbA1c (Model 2) outperformed the model with SF alone (Model 1), indicating a heightened predictive accuracy for GDM when these two biomarkers are used in combination. Conclusion The findings of this study highlight the potential utility of SF and HbA1c as early predictors of GDM risk, especially when employed in combination.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".