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Systematic review of model-based cervical screening evaluations

Overview of attention for article published in BMC Cancer, May 2015
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  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (75th percentile)

Mentioned by

10 tweeters
1 Facebook page


32 Dimensions

Readers on

137 Mendeley
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Systematic review of model-based cervical screening evaluations
Published in
BMC Cancer, May 2015
DOI 10.1186/s12885-015-1332-8
Pubmed ID

Diana Mendes, Iren Bains, Tazio Vanni, Mark Jit


Optimising population-based cervical screening policies is becoming more complex due to the expanding range of screening technologies available and the interplay with vaccine-induced changes in epidemiology. Mathematical models are increasingly being applied to assess the impact of cervical cancer screening strategies. We systematically reviewed MEDLINE®, Embase, Web of Science®, EconLit, Health Economic Evaluation Database, and The Cochrane Library databases in order to identify the mathematical models of human papillomavirus (HPV) infection and cervical cancer progression used to assess the effectiveness and/or cost-effectiveness of cervical cancer screening strategies. Key model features and conclusions relevant to decision-making were extracted. We found 153 articles meeting our eligibility criteria published up to May 2013. Most studies (72/153) evaluated the introduction of a new screening technology, with particular focus on the comparison of HPV DNA testing and cytology (n = 58). Twenty-eight in forty of these analyses supported HPV DNA primary screening implementation. A few studies analysed more recent technologies - rapid HPV DNA testing (n = 3), HPV DNA self-sampling (n = 4), and genotyping (n = 1) - and were also supportive of their introduction. However, no study was found on emerging molecular markers and their potential utility in future screening programmes. Most evaluations (113/153) were based on models simulating aggregate groups of women at risk of cervical cancer over time without accounting for HPV infection transmission. Calibration to country-specific outcome data is becoming more common, but has not yet become standard practice. Models of cervical screening are increasingly used, and allow extrapolation of trial data to project the population-level health and economic impact of different screening policy. However, post-vaccination analyses have rarely incorporated transmission dynamics. Model calibration to country-specific data is increasingly common in recent studies.

Twitter Demographics

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Mendeley readers

The data shown below were compiled from readership statistics for 137 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 1 <1%
United States 1 <1%
Netherlands 1 <1%
Brazil 1 <1%
Unknown 133 97%

Demographic breakdown

Readers by professional status Count As %
Student > Master 28 20%
Researcher 21 15%
Student > Ph. D. Student 15 11%
Student > Postgraduate 10 7%
Student > Doctoral Student 8 6%
Other 28 20%
Unknown 27 20%
Readers by discipline Count As %
Medicine and Dentistry 47 34%
Nursing and Health Professions 16 12%
Social Sciences 7 5%
Agricultural and Biological Sciences 5 4%
Business, Management and Accounting 5 4%
Other 23 17%
Unknown 34 25%

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 04 February 2016.
All research outputs
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Outputs from BMC Cancer
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Outputs of similar age
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Outputs of similar age from BMC Cancer
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Altmetric has tracked 13,293,718 research outputs across all sources so far. Compared to these this one has done well and is in the 75th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,977 research outputs from this source. They receive a mean Attention Score of 4.0. This one has done well, scoring higher than 83% 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 226,917 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 75% 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