ArticleGenetic epidemiology2014
Use of an electronic medical record to create the marshfield clinic twin/multiple birth cohort.
Article in Genetic epidemiology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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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
8 citing papers in PubMed, 14 citations in OpenAlex.
- Identifying Family Relationships from Electronic Health Records: A Machine Learning Approach.medRxiv : the preprint server for health sciences · 2026Article
- Enhancing patient representation learning with inferred family pedigrees improves disease risk prediction.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Automated Family Histories Significantly Improve Risk Prediction in an EHR.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2024Article
- Construction of Genealogical Knowledge Graphs From Obituaries: Multitask Neural Network Extraction System.Journal of medical Internet research · 2021Article
- Cardiac Anomalies in Liveborn and Stillborn Monochorionic Twins.Clinical medicine & research · 2020Article
- Article
- Applying family analyses to electronic health records to facilitate genetic research.Bioinformatics (Oxford, England) · 2018Article
- Phenome-wide association study maps new diseases to the human major histocompatibility complex region.Journal of medical genetics · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 3 institutions in 1 country.
Funding
Abstract
Population-based genetic analyses, such as the Genome-Wide Association Study (GWAS), have proven powerful for describing the genetic complexities of common disease in epidemiologic research. However, the significant challenges faced by population-based study designs have resulted in revitalization of family-based approaches, including twin studies. Twin studies are unique in their ability to ascertain both heritable and environmental contributions to human disease. Several regional and national twin registries have been constructed using a variety of methods to identify potential twins. A significant challenge in constructing these large twin registries includes the substantial resources required to recruit participants, collect phenotypic data, and update the registries as time progresses. Here we describe the use of the Marshfield Clinic electronic medical record (EMR) to identify a cohort of 19,226 patients enriched for twins or multiples. This cohort defines the Marshfield Clinic Twin/Multiple Birth Cohort (MCTC). An EMR system provides both a mechanism to identify potential twins and a source of detailed phenotypic data in near real time without the need for patient contact outside standard medical care. To demonstrate that the MCTC can be used for genetic-based epidemiologic research, concordance rates for muscular dystrophy (MD) and fragile-X syndrome-two highly heritable diseases-were assessed. Observations indicate that both MD and fragile-X syndrome are highly correlated among affected twins in the MCTC (P ≅ 3.7 × 10(-6) and 1.1 × 10(-4) , respectively). These findings suggest that EMR systems may not only be an effective resource for predicting families of twins, but can also be rapidly applied to epidemiologic research.
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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.