Evidence map›Paper›PMID 29926389›Full record

ArticleJournal of medical toxicology : official journal of the American College of Medical Toxicology2018

The Virtual Toxicology Journal Club: the Dissemination and Discussion of Noteworthy Manuscripts Using Twitter.

Peter R Chai, Anne-Michelle Ruha, Kelly E Wong, Derek L Monette, Meghan B Spyres, Jeff Lapoint, Howard Greller, Mark B Mycyk

Abstract read
In one paragraph

Article in Journal of medical toxicology : official journal of the American College of Medical Toxicology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
–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

5 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Buprenorphine Initiation in the Emergency Department: a Thematic Content Analysis of a #firesidetox Tweetchat.Journal of medical toxicology : official journal of the American College of Medical Toxicology · 2020
    Article
  4. Tweetchats, Disseminating Information, and Sparking Further Scientific Discussion with Social Media.Journal of medical toxicology : official journal of the American College of Medical Toxicology · 2020
    Article
  5. 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.

Peter R ChaiDivision of Medical Toxicology, Department of Emergency Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA. Pchai@bwh.harvard.edu.ORCID 0000-0003-0955-4117
Anne-Michelle RuhaDivision of Medical Toxicology and Precision Medicine, Department of Medicine, University of Arizona College of Medicine, Tucson, AZ, USA.
Kelly E WongDepartment of Emergency Medicine, Rhode Island Hospital, Brown University, Providence, USA.
Derek L MonetteDivision of Medical Toxicology, Department of Emergency Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA.
Meghan B SpyresDivision of Medical Toxicology, Department of Emergency Medicine, University of Southern California, Keck School of Medicine, Los Angeles, CA, USA.
Jeff LapointDivision of Medical Toxicology, Department of Emergency Medicine, Southern California Permanente Medical Group, Pasadena, CA, USA.
Howard GrellerDivision of Medical Toxicology, Department of Emergency Medicine, SBH Health Systems, New York City, NY, USA.
Mark B MycykDepartment of Emergency Medicine, Cook County Health and Hospitals System, Chicago, IL, USA.

Funding

Development of Ingestible Biosensors to Enhance PrEP Adherence in Substance UsersK23DA044874 · NIDA · BRIGHAM AND WOMEN'S HOSPITAL · PI CHAI, PETER R · 2018 to 2022
$1.0M
NIDA NIH HHS K23 DA044874NIDA NIH HHS K23DA044874
6 · The paper itself

Abstract

backgroundTwitter-based chat groups (tweetchats) structured as virtual journal clubs have been demonstrated to provide value to learners. In order to promote topics in medical toxicology, we developed the #firesidetox tweetchat as a virtual journal club to discuss and disseminate topics in medical toxicology.

methodsA group of medical toxicologists from the American College of Medical Toxicology (ACMT) Public Affairs Committee and editorial board of the Journal of Medical Toxicology (JMT) developed a quarterly one hour tweetchat featuring JMT manuscripts. We gathered basic twittergraphics and used a healthcare hashtag aggregator to measure the number of impressions, participants, and tweets per tweetchat session. A qualitative analysis of important themes from #firesidetox was also completed.

resultsDuring five tweetchats over 12 months, we attracted a mean of 23 participants generating a mean of 150 tweets per #firesidetox tweetchat. Tweets generated a mean of 329,200 impressions (unique user views): these impressions grew by 300% from the first through fifth #firesidetox. The majority of participants self-identified as medical toxicologists or physician learners. Although most were from the USA, participants also came from Australia, Poland, and Qatar. Most tweets centered on medical education and 7.9% tweets were learner-driven or questions asking for a medical toxicologist expert opinion.

conclusionThe #firesidetox attracted a diverse group of toxicologists, learners, and members of the public in a virtual journal club setting. The increasing number of impressions, participants, and tweets during #firesidetox demonstrates the tweetchat model to discuss pertinent toxicology topics is feasible and well received among its participants.

Indexed as

Social MediaToxicologyHumansInformation DisseminationInternetPhysiciansJournal clubsMedical educationToxicologyTwitter

Identifiers

PMID29926389
PMCPMC6097968

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.