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Faster title and abstract screening? Evaluating Abstrackr, a semi-automated online screening program for systematic reviewers

Overview of attention for article published in Systematic Reviews, June 2015
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (95th percentile)

Mentioned by

twitter
58 tweeters
peer_reviews
1 peer review site
facebook
1 Facebook page
googleplus
1 Google+ user

Citations

dimensions_citation
90 Dimensions

Readers on

mendeley
188 Mendeley
citeulike
2 CiteULike
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Title
Faster title and abstract screening? Evaluating Abstrackr, a semi-automated online screening program for systematic reviewers
Published in
Systematic Reviews, June 2015
DOI 10.1186/s13643-015-0067-6
Pubmed ID
Authors

John Rathbone, Tammy Hoffmann, Paul Glasziou

Abstract

Citation screening is time consuming and inefficient. We sought to evaluate the performance of Abstrackr, a semi-;automated online tool for predictive title and abstract screening. Four systematic reviews (aHUS, dietary fibre, ECHO, rituximab) were used to evaluate Abstrackr. Citations from electronic searches of biomedical databases were imported into Abstrackr, and titles and abstracts were screened and included or excluded according to the entry criteria. This process was continued until Abstrackr predicted and classified the remaining unscreened citations as relevant or irrelevant. These classification predictions were checked for accuracy against the original review decisions. Sensitivity analyses were performed to assess the effects of including case reports in the aHUS dataset whilst screening and the effects of using larger imbalanced datasets with the ECHO dataset. The performance of Abstrackr was calculated according to the number of relevant studies missed, the workload saving, and the precision of the algorithm to correctly predict relevant studies for inclusion, i.e. further full text inspection. Of the unscreened citations, Abstrackr's prediction algorithm correctly identified all relevant citations for the rituximab and dietary fibre reviews. However, one relevant citation in both the aHUS and ECHO reviews was incorrectly predicted as not relevant. The workload saving achieved with Abstrackr varied depending on the complexity and size of the reviews (9 % rituximab, 40 % dietary fibre, 67 % aHUS, and 57 % ECHO). The proportion of citations predicted as relevant, and therefore, warranting further full text inspection (i.e. the precision of the prediction) ranged from 16 % (aHUS) to 45 % (rituximab) and was affected by the complexity of the reviews. The false negative rate ranged from 2.4 to 21.7 %. Sensitivity analysis performed on the aHUS dataset increased the precision from 16 to 25 % and increased the workload saving by 10 % but increased the number of relevant studies missed. Sensitivity analysis performed with the larger ECHO dataset increased the workload saving (80 %) but reduced the precision (6.8 %) and increased the number of missed citations. Semi-automated title and abstract screening with Abstrackr has the potential to save time and reduce research waste.

Twitter Demographics

The data shown below were collected from the profiles of 58 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 188 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 1%
Netherlands 1 <1%
United Kingdom 1 <1%
Unknown 184 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 34 18%
Researcher 25 13%
Student > Master 25 13%
Librarian 17 9%
Student > Bachelor 12 6%
Other 43 23%
Unknown 32 17%
Readers by discipline Count As %
Medicine and Dentistry 48 26%
Computer Science 23 12%
Social Sciences 12 6%
Agricultural and Biological Sciences 12 6%
Psychology 9 5%
Other 40 21%
Unknown 44 23%

Attention Score in Context

This research output has an Altmetric Attention Score of 39. 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 24 May 2020.
All research outputs
#810,475
of 21,327,618 outputs
Outputs from Systematic Reviews
#116
of 1,845 outputs
Outputs of similar age
#9,185
of 204,558 outputs
Outputs of similar age from Systematic Reviews
#1
of 1 outputs
Altmetric has tracked 21,327,618 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 96th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,845 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 12.7. This one has done particularly well, scoring higher than 93% 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 204,558 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 95% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them