Evidence map›Paper›PMID 28699568›Full record

ArticleBMC medical informatics and decision making2017

Evaluation of the informatician perspective: determining types of research papers preferred by clinicians.

Boshu Ru, Xiaoyan Wang, Lixia Yao

Abstract read
In one paragraph

Article in BMC medical informatics and decision making, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

3 authors.

Boshu RuDepartment of Software and Information Systems, The University of North Carolina at Charlotte, Charlotte, NC, 28223, USA.
Xiaoyan WangDepartment of Family Medicine and Center for Quantitative Medicine, The University of Connecticut Health Center, Farmington, CT, 06030, USA.
Lixia YaoDepartment of Software and Information Systems, The University of North Carolina at Charlotte, Charlotte, NC, 28223, USA. yao.lixia@mayo.edu.

Funding

A research opportunity index to measure biomedical research disparities across the disease landscape.K01LM012102 · NLM · UNIVERSITY OF NORTH CAROLINA CHARLOTTE · PI YAO, LIXIA · 2016 to 2018
$535k
NLM NIH HHS K01 LM012102
6 · The paper itself

Abstract

backgroundTo deliver evidence-based medicine, clinicians often reference resources that are useful to their respective medical practices. Owing to their busy schedules, however, clinicians typically find it challenging to locate these relevant resources out of the rapidly growing number of journals and articles currently being published. The literature-recommender system may provide a possible solution to this issue if the individual needs of clinicians can be identified and applied.

methodsWe thus collected from the CiteULike website a sample of 96 clinicians and 6,221 scientific articles that they read. We examined the journal distributions, publication types, reading times, and geographic locations. We then compared the distributions of MeSH terms associated with these articles with those of randomly sampled MEDLINE articles using two-sample Z-test and multiple comparison correction, in order to identify the important topics relevant to clinicians.

resultsWe determined that the sampled clinicians followed the latest literature in a timely manner and read papers that are considered landmarks in medical research history. They preferred to read scientific discoveries from human experiments instead of molecular-, cellular- or animal-model-based experiments. Furthermore, the country of publication may impact reading preferences, particularly for clinicians from Egypt, India, Norway, Senegal, and South Africa.

conclusionThese findings provide useful guidance for developing personalized literature-recommender systems for clinicians.

Indexed as

Bibliographies as TopicBiomedical ResearchChoice BehaviorMedical Subject HeadingsMEDLINEHumansClinicians’ reading preferenceLiterature recommender systemsMedical subject headings

Identifiers

PMID28699568
PMCPMC5506573

What OpenQuestion holds

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LicenceCC BY
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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.