Evidence map›Paper›PMID 28567232›Full record

ArticleInternational journal of preventive medicine2017

Using Virtual Social Networks for Case Finding in Clinical Studies: An Experiment from Adolescence, Brain, Cognition, and Diabetes Study.

Ata Pourabbasi, Jalal Farzami, Mahbubeh-Sadat Ebrahimnegad Shirvani, Amir Hossein Shams, Bagher Larijani

Abstract read
In one paragraph

Article in International journal of preventive medicine, 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. Review
  2. "International journal of preventive medicine · 2022
    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

5 authors.

Ata PourabbasiDiabetes Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran.
Jalal FarzamiDepartment of Biostatistics, Faculty of Medical Sciences, Tarbiat Modarres University, Tehran, Iran.
Mahbubeh-Sadat Ebrahimnegad ShirvaniFoundation for Education, Research and Services on Educational Medicine and School Health, Tehran, Iran.
Amir Hossein ShamsDepartment of Biopharmaceutical Sciences, Faculty of Science, University of Ottawa, Canada.
Bagher LarijaniEndocrinology and Metabolism Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundOne of the main usages of social networks in clinical studies is facilitating the process of sampling and case finding for scientists. The main focus of this study is on comparing two different methods of sampling through phone calls and using social network, for study purposes.

methodsOne of the researchers started calling 214 families of children with diabetes during 90 days. After this period, phone calls stopped, and the team started communicating with families through telegram, a virtual social network for 30 days. The number of children who participated in the study was evaluated.

resultsAlthough the telegram method was 60 days shorter than the phone call method, researchers found that the number of participants from telegram (17.6%) did not have any significant differences compared with the ones being phone called (12.9%).

conclusionsUsing social networks can be suggested as a beneficial method for local researchers who look for easier sampling methods, winning their samples' trust, following up with the procedure, and an easy-access database.

Indexed as

Case findingclinical studiesdiabetessamplingsocial network

Identifiers

PMID28567232
PMCPMC5439290

What OpenQuestion holds

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Registered trials

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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.