Evidence map›Paper›PMID 33216120›Full record

ArticleJournal of the American Medical Informatics Association : JAMIA2021

The COVID-19 Trial Finder.

Yingcheng Sun, Alex Butler, Fengyang Lin, Hao Liu, Latoya A Stewart, Jae Hyun Kim, Betina Ross S Idnay, Qingyin Ge, Xinyi Wei, Cong Liu and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of the American Medical Informatics Association : JAMIA, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Automating Clinical Trial Matches Via Natural Language Processing of Synthetic Electronic Health Records and Clinical Trial Eligibility Criteria.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2024
    Article
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Yingcheng SunDepartment of Biomedical Informatics, Columbia University, New York, New York, USA.
Alex ButlerDepartment of Biomedical Informatics, Columbia University, New York, New York, USA.
Fengyang LinDepartment of Statistics, Columbia University, New York, New York, USA.
Hao LiuDepartment of Biomedical Informatics, Columbia University, New York, New York, USA.
Latoya A StewartVagelos College of Physicians and Surgeons, Columbia University, New York, New York, USA.
Jae Hyun KimDepartment of Biomedical Informatics, Columbia University, New York, New York, USA.
Betina Ross S IdnayDepartment of Neurology, Columbia University, New York, New York, USA.
Qingyin GeDepartment of Statistics, Columbia University, New York, New York, USA.
Xinyi WeiDepartment of Statistics, Columbia University, New York, New York, USA.
Cong LiuDepartment of Biomedical Informatics, Columbia University, New York, New York, USA.
Chi YuanDepartment of Biomedical Informatics, Columbia University, New York, New York, USA.
Chunhua WengDepartment of Biomedical Informatics, Columbia University, New York, New York, USA.

Funding

Clinical and Translational Science AwardUL1TR001873 · NCATS · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI REILLY, MUREDACH P · 2016 to 2025
$99.0M
Improving Clinical trial Education, Recruitment, and Enrollment at CTSA Hubs (I-CERCH) - HEAL SupplementU24TR001579 · NCATS · VANDERBILT UNIVERSITY MEDICAL CENTER · PI HARRIS, PAUL A., WILKINS, CONSUELO HOPKINS · 2016 to 2022
$34.1M
Reducing Health Disparities Through Informatics - Genomics SupplementT32NR007969 · NINR · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI SUZANNE BAKKEN, Rebecca Schnall · 2002 to 2026
$7.9M
Bridging the Semantic Gap Between Research Eligibility Criteria and Clinical DataR01LM009886 · NLM · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI WENG, CHUNHUA · 2009 to 2020
$5.3M
Reaching Communities through the Design of Information Visualizations (ReDIVis) Toolbox to Address COVID-19 Vaccine Hesitancy and Uptake.P30NR016587 · NINR · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI BAKKEN, SUZANNE, SMALDONE, ARLENE M · 2016 to 2022
$4.3M
NCATS NIH HHS U24 TR001579NCATS NIH HHS UL1 TR001873NINR NIH HHS P30 NR016587NINR NIH HHS T32 NR007969NLM NIH HHS R01 LM009886
6 · The paper itself

Abstract

Clinical trials are the gold standard for generating reliable medical evidence. The biggest bottleneck in clinical trials is recruitment. To facilitate recruitment, tools for patient search of relevant clinical trials have been developed, but users often suffer from information overload. With nearly 700 coronavirus disease 2019 (COVID-19) trials conducted in the United States as of August 2020, it is imperative to enable rapid recruitment to these studies. The COVID-19 Trial Finder was designed to facilitate patient-centered search of COVID-19 trials, first by location and radius distance from trial sites, and then by brief, dynamically generated medical questions to allow users to prescreen their eligibility for nearby COVID-19 trials with minimum human computer interaction. A simulation study using 20 publicly available patient case reports demonstrates its precision and effectiveness.

Indexed as

Clinical Trials as TopicCOVID-19Abstracting and IndexingAdultAgedAged, 80 and overChild, PreschoolEligibility DeterminationFemaleHumansInformation Storage and RetrievalMaleMiddle AgedPatient Selectionclinical trialCOVID-19eligibility criteriainformation filteringquestionnaireweb application

Identifiers

PMID33216120
PMCPMC7717322

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.