Evidence map›Paper›PMID 36758038›Full record

ArticlePloS one2023

Efficacy improvement in searching MEDLINE database using a novel PubMed visual analytic system: EEEvis.

Jong-Chan Lee, Brian J Lee, Changhee Park, Hyunjoo Song, Chan-Young Ock, Hyojae Sung, Sungjin Woo, Yuna Youn, Kwangrok Jung, Jae Hyup Jung and 5 more

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 2 pooled it
1.4field-weighted citation impact, top 20% 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

4 citing papers in PubMed, 2 syntheses or guidelines pooled it, 9 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Article
  4. Review
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

15 authors at 4 institutions in 1 country.

Jong-Chan LeeDepartment of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Korea.
Brian J LeeDepartment of Computer Science & Engineering, Seoul National University, Seoul, Korea.
Changhee ParkDepartment of Internal Medicine, Seoul National University Hospital, Seoul, Korea.
Hyunjoo SongSchool of Computer Science & Engineering, Soongsil University, Seoul, Korea.ORCID 0000-0002-4931-2940
Chan-Young OckBang & Ock Consulting, Inc., Seoul, Korea.
Hyojae SungCollege of Medicine, Seoul National University, Seoul, Korea.
Sungjin WooCollege of Medicine, Seoul National University, Seoul, Korea.
Yuna YounDepartment of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Korea.
Kwangrok JungDepartment of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Korea.
Jae Hyup JungDepartment of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Korea.
Jinwoo AhnDepartment of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Korea.
Bomi KimDepartment of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Korea.
Jaihwan KimDepartment of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Korea.
Jinwook SeoDepartment of Computer Science & Engineering, Seoul National University, Seoul, Korea.
Jin-Hyeok HwangDepartment of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Korea.ORCID 0000-0002-5643-8461
Seoul National University · KRSeoul National University Bundang Hospital · KRSeoul National University Hospital · KRSoongsil University · KR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

PubMed is the most extensively used database and search engine in the biomedical and healthcare fields. However, users could experience several difficulties in acquiring their target papers facing massive numbers of search results, especially in their unfamiliar fields. Therefore, we developed a novel user interface for PubMed and conducted three steps of study: step A, a preliminary user survey with 76 medical experts regarding the current usability for the biomedical literature search task at PubMed; step B is implementing EEEvis, a novel interactive visual analytic system for the search task; step C, a randomized user study comparing PubMed and EEEvis. First, we conducted a Google survey of 76 medical experts regarding the unmet needs of PubMed and the user requirements for a novel search interface. According to the data of preliminary Google survey, we implemented a novel interactive visual analytic system for biomedical literature search. This EEEvis provides enhanced literature data analysis functions including (1) an overview of the bibliographic features including publication date, citation count, and impact factors, (2) an overview of the co-authorship network, and (3) interactive sorting, filtering, and highlighting. In the randomized user study of 24 medical experts, the search speed of EEEvis was not inferior to PubMed in the time to reach the first article (median difference 3 sec, 95% CI -2.1 to 8.5, P = 0.535) nor in the search completion time (median difference 8 sec, 95% CI -4.7 to 19.1, P = 0.771). However, 22 participants (91.7%) responded that they are willing to use EEEvis as their first choice for a biomedical literature search task, and 21 participants (87.5%) answered the bibliographic sorting and filtering functionalities of EEEvis as a major advantage. EEEvis could be a supplementary interface for PubMed that can enhance the user experience in the search for biomedical literature.

Indexed as

Search EngineDatabases, FactualHumansMEDLINEPubMed

Identifiers

PMID36758038
PMCPMC9910730
OpenAlexW4319656237

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.