Evidence map›Paper›PMID 30295669›Full record

ArticleJournal of visualized experiments : JoVE2018

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts.

John Harry Caufield, David A Liem, Anders O Garlid, Yijiang Zhou, Karol Watson, Alex A T Bui, Wei Wang, Peipei Ping

Abstract readVideo-Audio Media
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of visualized experiments : JoVE, 2018. 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. Review
  2. Article
  3. A manual corpus of annotated main findings of clinical case reports.Database : the journal of biological databases and curation · 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

8 authors.

John Harry CaufieldThe NIH BD2K Center of Excellence in Biomedical Computing, University of California, Los Angeles; Department of Physiology, University of California, Los Angeles; jcaufield@mednet.ucla.edu.
David A LiemThe NIH BD2K Center of Excellence in Biomedical Computing, University of California, Los Angeles; Department of Physiology, University of California, Los Angeles; Department of Medicine/Cardiology, University of California, Los Angeles.
Anders O GarlidThe NIH BD2K Center of Excellence in Biomedical Computing, University of California, Los Angeles; Department of Physiology, University of California, Los Angeles.
Yijiang ZhouDepartment of Cardiology, First Affiliated Hospital, Zhejiang University School of Medicine.
Karol WatsonThe NIH BD2K Center of Excellence in Biomedical Computing, University of California, Los Angeles; Department of Medicine/Cardiology, University of California, Los Angeles.
Alex A T BuiThe NIH BD2K Center of Excellence in Biomedical Computing, University of California, Los Angeles; Department of Radiological Sciences, University of California, Los Angeles; Department of Bioengineering, University of California, Los Angeles; Scalable Analytics Institute (ScAi), University of California, Los Angeles.
Wei WangThe NIH BD2K Center of Excellence in Biomedical Computing, University of California, Los Angeles; Scalable Analytics Institute (ScAi), University of California, Los Angeles; Department of Bioinformatics, University of California, Los Angeles; Department of Computer Science, University of California, Los Angeles.
Peipei PingThe NIH BD2K Center of Excellence in Biomedical Computing, University of California, Los Angeles; Department of Physiology, University of California, Los Angeles; Department of Medicine/Cardiology, University of California, Los Angeles; Scalable Analytics Institute (ScAi), University of California, Los Angeles; Department of Bioinformatics, University of California, Los Angeles.

Funding

TRAININGU54GM114833 · NIGMS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI LINDSEY, MERRY L., PING, PEIPEI · 2014 to 2017
$13.8M
Omics Phenotyping for Identifying Molecular Signatures of the Healthy and Failing Heart: An Integrated Data Science PlatformR35HL135772 · NHLBI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI PING, PEIPEI · 2017 to 2023
$6.4M
Medical Imaging Informatics Training GrantT32EB016640 · NIBIB · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI ALEX BUI, William Hsu · 2013 to 2026
$2.3M
NHLBI NIH HHS R35 HL135772NIBIB NIH HHS T32 EB016640NIGMS NIH HHS U54 GM114833
6 · The paper itself

Abstract

Clinical case reports (CCRs) are a valuable means of sharing observations and insights in medicine. The form of these documents varies, and their content includes descriptions of numerous, novel disease presentations and treatments. Thus far, the text data within CCRs is largely unstructured, requiring significant human and computational effort to render these data useful for in-depth analysis. In this protocol, we describe methods for identifying metadata corresponding to specific biomedical concepts frequently observed within CCRs. We provide a metadata template as a guide for document annotation, recognizing that imposing structure on CCRs may be pursued by combinations of manual and automated effort. The approach presented here is appropriate for organization of concept-related text from a large literature corpus (e.g., thousands of CCRs) but may be easily adapted to facilitate more focused tasks or small sets of reports. The resulting structured text data includes sufficient semantic context to support a variety of subsequent text analysis workflows: meta-analyses to determine how to maximize CCR detail, epidemiological studies of rare diseases, and the development of models of medical language may all be made more realizable and manageable through the use of structured text data.

Indexed as

MetadataHumansSemantics

Identifiers

PMID30295669
PMCPMC6235242

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.