ReviewHuman molecular genetics2020
Implicit bias of encoded variables: frameworks for addressing structured bias in EHR-GWAS data.
Review in Human molecular genetics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 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
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Implicit bias in referrals to relational psychological therapies: review and recommendations for mental health services.Frontiers in public health · 2024Pooled it
- Genomic meta-analyses of binge-eating behavior and anorexia nervosa yield insights into the unique and shared biology of eating disorder phenotypes.Nature. Mental health · 2026Article
- Transforming neonatal care through informatics: A review of artificial intelligence, data, and implementation considerations.Seminars in perinatology · 2025Review
- Psychiatric disorders: teasing apart genetic similarities and differences.Trends in genetics : TIG · 2025Article
- Assessment and ascertainment in psychiatric molecular genetics: challenges and opportunities for cross-disorder research.Molecular psychiatry · 2025Review
- Bias recognition and mitigation strategies in artificial intelligence healthcare applications.NPJ digital medicine · 2025Review
- Implications of gene × environment interactions in post-traumatic stress disorder risk and treatment.The Journal of clinical investigation · 2025Review
- Comorbidity Profiles of Posttraumatic Stress Disorder Across the Medical Phenome.Biological psychiatry global open science · 2024Article
- Inherent Bias in Electronic Health Records: A Scoping Review of Sources of Bias.medRxiv : the preprint server for health sciences · 2024Article
- Open Science Practices in Psychiatric Genetics: A Primer.Biological psychiatry global open science · 2024Review
- Digital Determinants of Health: Health data poverty amplifies existing health disparities-A scoping review.PLOS digital health · 2023Article
- Comorbidity Profiles of Posttraumatic Stress Disorder Across the Medical Phenome.medRxiv : the preprint server for health sciences · 2023Article
- Detecting and Adjusting for Hidden Biases due to Phenotype Misclassification in Genome-Wide Association Studies.medRxiv : the preprint server for health sciences · 2023Article
- What next for eating disorder genetics? Replacing myths with facts to sharpen our understanding.Molecular psychiatry · 2022Review
- Penetrance and Pleiotropy of Polygenic Risk Scores for Schizophrenia, Bipolar Disorder, and Depression Among Adults in the US Veterans Affairs Health Care System.JAMA psychiatry · 2022Article
- Cohort design and natural language processing to reduce bias in electronic health records research.NPJ digital medicine · 2022Article
- Psychiatric genomics, mental health equity, and intersectionality: A framework for research and practice.Frontiers in psychiatry · 2022Article
- Thoughtful Phenotype Definitions Empower Participants and Power Studies.Complex psychiatryArticle
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.
Funding
Abstract
The 'discovery' stage of genome-wide association studies required amassing large, homogeneous cohorts. In order to attain clinically useful insights, we must now consider the presentation of disease within our clinics and, by extension, within our medical records. Large-scale use of electronic health record (EHR) data can help to understand phenotypes in a scalable manner, incorporating lifelong and whole-phenome context. However, extending analyses to incorporate EHR and biobank-based analyses will require careful consideration of phenotype definition. Judgements and clinical decisions that occur 'outside' the system inevitably contain some degree of bias and become encoded in EHR data. Any algorithmic approach to phenotypic characterization that assumes non-biased variables will generate compounded biased conclusions. Here, we discuss and illustrate potential biases inherent within EHR analyses, how these may be compounded across time and suggest frameworks for large-scale phenotypic analysis to minimize and uncover encoded bias.
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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.