ArticleBMC endocrine disorders2026
Development and validation of a multidimensional indicator-based risk prediction model for gestational diabetes mellitus: a nested case-control study.
Article in BMC endocrine disorders, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
backgroundGestational diabetes mellitus (GDM) could contribute to significant health risks in both mothers and their offspring. Therefore, this study aims to construct a prediction model to identify women at elevated risk for GDM in early pregnancy.
methodsMethods: This study was a nested case-control study. 346 participants were randomly allocated to the training set (n = 242) and the validation set (n = 104) at a ratio of 7:3. The least absolute shrinkage and selection operator (LASSO) regression was applied to select the most significant factors among candidate variables. A GDM risk prediction model was further established based on the risk factors chosen by the LASSO. The model’s calibration, discrimination, and clinical use were assessed using the calibration analysis, area under the receiver operating characteristic (ROC) curve and decision curve analysis (DCA). Finally, the model was presented with a nomogram.
resultsIn the training set, a simple GDM risk prediction model was developed by using family history of diabetes, pre-pregnancy body mass index (BMI), progesterone, aspartate transaminase (AST), activated partial thromboplastin time (APTT), and triglyceride to high-density lipoprotein cholesterol (TG/HDL-C). Among them, family history of diabetes, higher pre-pregnancy BMI, progesterone, AST, and TG/HDL-C levels were associated with increased GDM risk, while higher APTT level was associated with decreased GDM risk. The calibration curve indicated satisfactory accuracy. The ROC curve demonstrated excellent discrimination, with the area under the curve (AUC) of 0.85 (95% confidence interval [CI], 0.80–0.91) and 0.73 (95%CI, 0.62–0.83) for the training and validation set, respectively. The DCA curve demonstrated high net benefit. Furthermore, internal validation with excellent performance demonstrated the generalizability of the model.
conclusionsThe present study developed a model with excellent performance for predicting GDM. Furthermore, a nomogram was constructed to visualize the model. Therefore, this model can serve as an effective GDM prediction tool.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.