Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.
2 · The registry
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.
Elizabeth S LangenDepartment of Obstetrics and Gynecology, University of Michigan, Ann Arbor, MI, USA.
Dominick J LemasDepartment of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL, USA.
Lana X GarmireDepartment of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA. lgarmire@umich.edu.ORCID http://orcid.org/0000-0002-4654-2126
Funding
Using social networks to map and evaluate team science across CTSA hubsUL1TR001427 · NCATS · UNIVERSITY OF FLORIDA · PI MITCHELL, DUANE A. · 2015 to 2024
$37.2M
Together: Transforming and Translating Discovery to Improve HealthTL1TR001428 · NCATS · UNIVERSITY OF FLORIDA · PI MCCORMACK, WAYNE T. · 2015 to 2023
$3.9M
15/24- Healthy Brain and Child Development National ConsortiumU01DA055358 · NIDA · UNIVERSITY OF FLORIDA · PI GURKA, KELLY K., SCOTT, LISA S. · 2021 to 2023
$3.7M
An Integrative Omics Approach to Identify Biomarkers Related to Preeclampsia and Breast Cancer RisksR01HD084633 · NICHD · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI GARMIRE, LANA X · 2016 to 2020
$3.0M
Biomedical Informatics and Data Science Training Program (BIDS-TP)T32GM141746 · NIGMS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Ivo D Dinov, RYAN E MILLS · 2021 to 2026
$2.6M
Cancer precision medicine through spatially informative single cell image and transcriptomics data analysisR01LM012373 · NLM · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI GARMIRE, LANA X · 2016 to 2024
$2.4M
DR. EPS: Drug Repurposing for Extended Patient SurvivalR01LM012907 · NLM · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI GARMIRE, LANA X · 2019 to 2022
$1.0M
Human milk metabolomics and microbe-host interactions associated with pediatric obesityK01DK115632 · NIDDK · UNIVERSITY OF FLORIDA · PI LEMAS, DOMINICK JOSEPH · 2019 to 2023
$831k
Clinical, Histological, and Transcriptional Associations Between the Diabetic Placenta and Fetal Congenital Heart DefectsF30HL172988 · NHLBI · UNIVERSITY OF FLORIDA · PI ADITYA Devidas Mahadevan · 2024 to 2026
$142k
NCATS NIH HHS TL1 TR001428NCATS NIH HHS UL1 TR001427NHLBI NIH HHS F30 HL172988NICHD NIH HHS R01 HD084633NIDA NIH HHS U01 DA055358NIDDK NIH HHS K01 DK115632NIGMS NIH HHS T32 GM141746NLM NIH HHS R01 LM012373NLM NIH HHS R01 LM012907U.S. Department of Health & Human Services | NIH | Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) R01HD084633U.S. Department of Health & Human Services | NIH | National Institute of Diabetes and Digestive and Kidney Diseases (National Institute of Diabetes & Digestive & Kidney Diseases) K01DK115632U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (NIGMS) T32GM141746U.S. Department of Health & Human Services | NIH | U.S. National Library of Medicine (NLM) R01LM012373U.S. Department of Health & Human Services | NIH | U.S. National Library of Medicine (NLM) R01LM012907
6 · The paper itself
Abstract
Preeclampsia is a major cause of maternal and perinatal mortality with no known cure. Delivery timing is critical to balancing maternal and fetal risks. We develop and externally validate PEDeliveryTime, a class of clinically informative models which resulted from deep-learning models, to predict the time from PE diagnosis to delivery using electronic health records. We build the models on 1533 PE cases from the University of Michigan and validate it on 2172 preeclampsia cases from the University of Florida. PEDeliveryTime full model contains only 12 features yet achieves high c-index of 0.79 and 0.74 on the Michigan and Florida data set respectively. For the early-onset preeclampsia subset, the full model reaches 0.76 and 0.67 on the Michigan and Florida test sets. Collectively, these models perform an early assessment of delivery urgency and might help to better prioritize medical resources.
Indexed as
Delivery, ObstetricElectronic Health RecordsPre-EclampsiaAdultDeep LearningFemaleFloridaHumansMichiganPregnancyTime Factors
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.
Predicting interval from diagnosis to delivery in preeclampsia using electronic health records. · full record | OpenQuestion