Evidence map›Paper›PMID 26269536›Full record

ArticleJournal of the American Medical Informatics Association : JAMIA2016

Assessing the readability of ClinicalTrials.gov.

Danny T Y Wu, David A Hanauer, Qiaozhu Mei, Patricia M Clark, Lawrence C An, Joshua Proulx, Qing T Zeng, V G Vinod Vydiswaran, Kevyn Collins-Thompson, Kai Zheng

Open access · hybridAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of the American Medical Informatics Association : JAMIA, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
30citing papers in PubMed, 1 pooled it
6.3field-weighted citation impact, top 4% of its field
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

30 citing papers in PubMed, 1 synthesis or guideline pooled it, 47 citations in OpenAlex.

  1. Pooled it
  2. AI-based augmentation of oncology clinical trials.Nature reviews. Clinical oncology · 2026
    Review
  3. KRAS Clinical Trial Finder: A Patient-Centered Platform for Improving Access to Clinical Trial Information.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2026
    Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Clinical Research Informatics: a Decade-in-Review.Yearbook of medical informatics · 2024
    Review
  9. Article
  10. Named Entity Recognition and Normalization for Alzheimer's Disease Eligibility Criteria.Proceedings. IEEE International Conference on Healthcare Informatics · 2023
    Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Primary care perspectives on implementation of clinical trial recruitment.Journal of clinical and translational science · 2020
    Article
  19. Article
  20. Assessing the Readability of Freely Available ICU Notes.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2019
    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

10 authors at 2 institutions in 1 country.

Danny T Y WuSchool of Information, University of Michigan, Ann Arbor, MI, USA.
David A HanauerSchool of Information, University of Michigan, Ann Arbor, MI, USA Department of Pediatrics, University of Michigan, Ann Arbor, MI, USA.
Qiaozhu MeiSchool of Information, University of Michigan, Ann Arbor, MI, USA Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, USA.
Patricia M ClarkSchool of Nursing, University of Michigan, Ann Arbor, MI, USA Center for Health Communication Research, University of Michigan, Ann Arbor, MI, USA.
Lawrence C AnCenter for Health Communication Research, University of Michigan, Ann Arbor, MI, USA Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA.
Joshua ProulxDepartment of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA.
Qing T ZengDepartment of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA.
V G Vinod VydiswaranSchool of Information, University of Michigan, Ann Arbor, MI, USA.
Kevyn Collins-ThompsonSchool of Information, University of Michigan, Ann Arbor, MI, USA Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, USA.
Kai ZhengSchool of Information, University of Michigan, Ann Arbor, MI, USA School of Public Health Department of Health Management and Policy, University of Michigan, Ann Arbor, MI, USA kzheng@umich.edu.
University of Michigan–Ann Arbor · USUniversity of Utah · US

Funding

Michigan Institute for Clinical and Health Research (MCHR)UL1TR000433 · NCATS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI MASHOUR, GEORGE ALEXANDER · 2012 to 2016
$49.9M
NCATS NIH HHS 2UL1TR000433
6 · The paper itself

Abstract

objectiveClinicalTrials.gov serves critical functions of disseminating trial information to the public and helping the trials recruit participants. This study assessed the readability of trial descriptions at ClinicalTrials.gov using multiple quantitative measures. MATERIALS AND

methodsThe analysis included all 165,988 trials registered at ClinicalTrials.gov as of April 30, 2014. To obtain benchmarks, the authors also analyzed 2 other medical corpora: (1) all 955 Health Topics articles from MedlinePlus and (2) a random sample of 100,000 clinician notes retrieved from an electronic health records system intended for conveying internal communication among medical professionals. The authors characterized each of the corpora using 4 surface metrics, and then applied 5 different scoring algorithms to assess their readability. The authors hypothesized that clinician notes would be most difficult to read, followed by trial descriptions and MedlinePlus Health Topics articles.

resultsTrial descriptions have the longest average sentence length (26.1 words) across all corpora; 65% of their words used are not covered by a basic medical English dictionary. In comparison, average sentence length of MedlinePlus Health Topics articles is 61% shorter, vocabulary size is 95% smaller, and dictionary coverage is 46% higher. All 5 scoring algorithms consistently rated CliniclTrials.gov trial descriptions the most difficult corpus to read, even harder than clinician notes. On average, it requires 18 years of education to properly understand these trial descriptions according to the results generated by the readability assessment algorithms. DISCUSSION AND

conclusionTrial descriptions at CliniclTrials.gov are extremely difficult to read. Significant work is warranted to improve their readability in order to achieve CliniclTrials.gov's goal of facilitating information dissemination and subject recruitment.

Indexed as

Clinical Trials as TopicComprehensionDatabases, FactualVocabularyAlgorithmsAnalysis of VarianceConsumer Health InformationMedlinePlusTerminology as Topicclinical trialCliniclTrials.govcomprehensionelectronic health recordsnatural language processingreadability

Identifiers

PMID26269536
PMCPMC5009924
OpenAlexW2115836457

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.