ArticleJournal of perinatology : official journal of the California Perinatal Association2016
Predicting failure of glyburide therapy in gestational diabetes.
Article in Journal of perinatology : official journal of the California Perinatal Association, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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
5 citing papers in PubMed, 1 synthesis or guideline pooled it, 12 citations in OpenAlex.
- Prediction Models for Maternal and Offspring Short- and Long-Term Outcomes Following Gestational Diabetes: A Systematic Review.Obesity reviews : an official journal of the International Association for the Study of Obesity · 2025Pooled it
- Glibenclamide and metfoRmin versus stAndard care in gEstational diabeteS (GRACES): a feasibility open label randomised trial.BMC pregnancy and childbirth · 2017Trial
- The use of machine learning to predict pharmacological therapy in gestational diabetes: A scoping review.Diabetic medicine : a journal of the British Diabetic Association · 2026Review
- Precision gestational diabetes treatment: a systematic review and meta-analyses.Communications medicine · 2023Article
- Comparison of Multivariable Logistic Regression and Other Machine Learning Algorithms for Prognostic Prediction Studies in Pregnancy Care: Systematic Review and Meta-Analysis.JMIR medical informatics · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
objectiveWe sought to develop a prediction model to identify women with gestational diabetes (GDM) who require insulin to achieve glycemic control. STUDY
designRetrospective cohort of all singletons with GDM treated with glyburide from 2007 to 2013. Glyburide failure was defined as reaching glyburide 20 mg day(-1) and receiving insulin. Glyburide success was defined as any glyburide dose without insulin and >70% of visits with glycemic control. Multivariable logistic regression analysis was performed to create a prediction model.
resultOf the 360 women, 63 (17.5%) qualified as glyburide failure and 157 (43.6%) as glyburide success. The final prediction model for glyburide failure included prior GDM, GDM diagnosis ⩽26 weeks, 1-h glucose challenge test ⩾228 mg dl(-1), 3-h glucose tolerance test 1-h value ⩾221 mg dl(-1), ⩾7 postprandial blood sugars >120 mg dl(-1) in the week glyburide started and ⩾1 blood sugar >200 mg dl(-1). The model accurately classified 81% of subjects.
conclusionWomen with GDM who will require insulin can be identified at the initiation of pharmacological therapy.
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.