ArticleJournal of medical Internet research2015
Ranking adverse drug reactions with crowdsourcing.
Article in Journal of medical Internet research, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 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
24 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Mapping of Crowdsourcing in Health: Systematic Review.Journal of medical Internet research · 2018Pooled it
- Improving Electronic Health Record Note Comprehension With NoteAid: Randomized Trial of Electronic Health Record Note Comprehension Interventions With Crowdsourced Workers.Journal of medical Internet research · 2019Trial
- Development of a genetic priority score to predict drug side effects using human genetic evidence.Nature communications · 2025Article
- Human genetic evidence enriched for side effects of approved drugs.PLoS genetics · 2025Article
- Individual Factors That Affect Laypeople's Understanding of Definitions of Medical Jargon.Health policy and technology · 2024Article
- Article
- Genome-first evaluation with exome sequence and clinical data uncovers underdiagnosed genetic disorders in a large healthcare system.Cell reports. Medicine · 2024Article
- Mining Real-World Big Data to Characterize Adverse Drug Reaction Quantitatively: Mixed Methods Study.Journal of medical Internet research · 2024Article
- Quantifying the Severity of Adverse Drug Reactions Using Social Media: Network Analysis.Journal of medical Internet research · 2021Article
- Using Machine Learning to Identify Adverse Drug Effects Posing Increased Risk to Women.Patterns (New York, N.Y.) · 2020Article
- Precision Telemedicine through Crowdsourced Machine Learning: Testing Variability of Crowd Workers for Video-Based Autism Feature Recognition.Journal of personalized medicine · 2020Article
- The development of a scoring and ranking strategy for a patient-tailored adverse drug reaction prediction in polypharmacy.Scientific reports · 2020Article
- Development and Validation of a Comprehensive Well-Being Scale for People in the University Environment (Pitt Wellness Scale) Using a Crowdsourcing Approach: Cross-Sectional Study.Journal of medical Internet research · 2020Article
- Applications of crowdsourcing in health: an overview.Journal of global health · 2018Review
- ComprehENotes, an Instrument to Assess Patient Reading Comprehension of Electronic Health Record Notes: Development and Validation.Journal of medical Internet research · 2018Article
- Prioritizing research topics: a comparison of crowdsourcing and patient registry.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2018Article
- Adverse Drug Event Discovery Using Biomedical Literature: A Big Data Neural Network Adventure.JMIR medical informatics · 2017Article
- North American Public Opinion Survey on the Acceptability of Crowdsourcing Basic Life Support for Out-of-Hospital Cardiac Arrest With the PulsePoint Mobile Phone App.JMIR mHealth and uHealth · 2017Article
- Extraction and analysis of signatures from the Gene Expression Omnibus by the crowd.Nature communications · 2016Article
- Consumers' Patient Portal Preferences and Health Literacy: A Survey Using Crowdsourcing.JMIR research protocols · 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
4 authors.
Funding
Abstract
backgroundThere is no publicly available resource that provides the relative severity of adverse drug reactions (ADRs). Such a resource would be useful for several applications, including assessment of the risks and benefits of drugs and improvement of patient-centered care. It could also be used to triage predictions of drug adverse events.
objectiveThe intent of the study was to rank ADRs according to severity.
methodsWe used Internet-based crowdsourcing to rank ADRs according to severity. We assigned 126,512 pairwise comparisons of ADRs to 2589 Amazon Mechanical Turk workers and used these comparisons to rank order 2929 ADRs.
resultsThere is good correlation (rho=.53) between the mortality rates associated with ADRs and their rank. Our ranking highlights severe drug-ADR predictions, such as cardiovascular ADRs for raloxifene and celecoxib. It also triages genes associated with severe ADRs such as epidermal growth-factor receptor (EGFR), associated with glioblastoma multiforme, and SCN1A, associated with epilepsy.
conclusionsADR ranking lays a first stepping stone in personalized drug risk assessment. Ranking of ADRs using crowdsourcing may have useful clinical and financial implications, and should be further investigated in the context of health care decision making.
Indexed as
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What Socratic 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 Socratic graph.