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Characteristics and knowledge synthesis approach for 456 network meta-analyses: a scoping review

Overview of attention for article published in BMC Medicine, January 2017
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (91st percentile)
  • Good Attention Score compared to outputs of the same age and source (68th percentile)

Mentioned by

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38 tweeters

Citations

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43 Dimensions

Readers on

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65 Mendeley
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Title
Characteristics and knowledge synthesis approach for 456 network meta-analyses: a scoping review
Published in
BMC Medicine, January 2017
DOI 10.1186/s12916-016-0764-6
Pubmed ID
Authors

Wasifa Zarin, Areti Angeliki Veroniki, Vera Nincic, Afshin Vafaei, Emily Reynen, Sanober S. Motiwala, Jesmin Antony, Shannon M. Sullivan, Patricia Rios, Caitlin Daly, Joycelyne Ewusie, Maria Petropoulou, Adriani Nikolakopoulou, Anna Chaimani, Georgia Salanti, Sharon E. Straus, Andrea C. Tricco

Abstract

Network meta-analysis (NMA) has become a popular method to compare more than two treatments. This scoping review aimed to explore the characteristics and methodological quality of knowledge synthesis approaches underlying the NMA process. We also aimed to assess the statistical methods applied using the Analysis subdomain of the ISPOR checklist. Comprehensive literature searches were conducted in MEDLINE, PubMed, EMBASE, and Cochrane Database of Systematic Reviews from inception until April 14, 2015. References of relevant reviews were scanned. Eligible studies compared at least four different interventions from randomised controlled trials with an appropriate NMA approach. Two reviewers independently performed study selection and data abstraction of included articles. All discrepancies between reviewers were resolved by a third reviewer. Data analysis involved quantitative (frequencies) and qualitative (content analysis) methods. Quality was evaluated using the AMSTAR tool for the conduct of knowledge synthesis and the ISPOR tool for statistical analysis. After screening 3538 citations and 877 full-text papers, 456 NMAs were included. These were published between 1997 and 2015, with 95% published after 2006. Most were conducted in Europe (51%) or North America (31%), and approximately one-third reported public sources of funding. Overall, 84% searched two or more electronic databases, 62% searched for grey literature, 58% performed duplicate study selection and data abstraction (independently), and 62% assessed risk of bias. Seventy-eight (17%) NMAs relied on previously conducted systematic reviews to obtain studies for inclusion in their NMA. Based on the AMSTAR tool, almost half of the NMAs incorporated quality appraisal results to formulate conclusions, 36% assessed publication bias, and 16% reported the source of funding. Based on the ISPOR tool, half of the NMAs did not report if an assessment for consistency was conducted or whether they accounted for inconsistency when present. Only 13% reported heterogeneity assumptions for the random-effects model. The knowledge synthesis methods and analytical process for NMAs are poorly reported and need improvement.

Twitter Demographics

The data shown below were collected from the profiles of 38 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 65 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 65 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 14 22%
Researcher 11 17%
Student > Master 5 8%
Student > Bachelor 4 6%
Student > Doctoral Student 4 6%
Other 14 22%
Unknown 13 20%
Readers by discipline Count As %
Medicine and Dentistry 18 28%
Nursing and Health Professions 8 12%
Mathematics 3 5%
Sports and Recreations 2 3%
Agricultural and Biological Sciences 2 3%
Other 12 18%
Unknown 20 31%

Attention Score in Context

This research output has an Altmetric Attention Score of 20. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 02 December 2017.
All research outputs
#1,075,547
of 16,479,073 outputs
Outputs from BMC Medicine
#856
of 2,606 outputs
Outputs of similar age
#34,756
of 390,905 outputs
Outputs of similar age from BMC Medicine
#67
of 214 outputs
Altmetric has tracked 16,479,073 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 93rd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,606 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 38.0. This one has gotten more attention than average, scoring higher than 67% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 390,905 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 91% of its contemporaries.
We're also able to compare this research output to 214 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 68% of its contemporaries.