Lack of Data Sharing Despite Data Availability Statements in Studies Using Machine Learning Models for Prediction of Gestational Diabetes Mellitus.

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

4 citing papers in PubMed.

1. PKRxiv: A Best Practice Model for Advancing Pharmacoequity Through Open Pharmacokinetic Data Sharing.Clinical pharmacology and therapeutics · 2026
   Article
2. Advances in gestational diabetes mellitus screening: Emerging trends and future directions.World journal of diabetes · 2025
   Review
3. Label Accuracy in Electronic Health Records and Its Impact on Machine Learning Models for Early Prediction of Gestational Diabetes: 3-Step Retrospective Validation Study.JMIR medical informatics · 2025
   Article
4. Evaluation of machine learning models for early prediction of gestational diabetes using retrospective electronic health records from current and previous pregnancies.BMJ digital health & AI · 2025
   Article

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

Authors and funding

3 authors.

Funding

Abstract

PubMed holds no abstract for this paper.

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