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An updated PREDICT breast cancer prognostication and treatment benefit prediction model with independent validation

Overview of attention for article published in Breast Cancer Research, May 2017
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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 (73rd percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

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10 tweeters

Citations

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121 Dimensions

Readers on

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131 Mendeley
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Title
An updated PREDICT breast cancer prognostication and treatment benefit prediction model with independent validation
Published in
Breast Cancer Research, May 2017
DOI 10.1186/s13058-017-0852-3
Pubmed ID
Authors

Francisco J. Candido dos Reis, Gordon C. Wishart, Ed M. Dicks, David Greenberg, Jem Rashbass, Marjanka K. Schmidt, Alexandra J. van den Broek, Ian O. Ellis, Andrew Green, Emad Rakha, Tom Maishman, Diana M. Eccles, Paul D. P. Pharoah, Candido dos Reis, Francisco J., Wishart, Gordon C., Dicks, Ed M., Greenberg, David, Rashbass, Jem, Schmidt, Marjanka K., van den Broek, Alexandra J., Ellis, Ian O., Green, Andrew, Rakha, Emad, Maishman, Tom, Eccles, Diana M., Pharoah, Paul D. P.

Abstract

PREDICT is a breast cancer prognostic and treatment benefit model implemented online. The overall fit of the model has been good in multiple independent case series, but PREDICT has been shown to underestimate breast cancer specific mortality in women diagnosed under the age of 40. Another limitation is the use of discrete categories for tumour size and node status resulting in 'step' changes in risk estimates on moving between categories. We have refitted the PREDICT prognostic model using the original cohort of cases from East Anglia with updated survival time in order to take into account age at diagnosis and to smooth out the survival function for tumour size and node status. Multivariable Cox regression models were used to fit separate models for ER negative and ER positive disease. Continuous variables were fitted using fractional polynomials and a smoothed baseline hazard was obtained by regressing the baseline cumulative hazard for each patients against time using fractional polynomials. The fit of the prognostic models were then tested in three independent data sets that had also been used to validate the original version of PREDICT. In the model fitting data, after adjusting for other prognostic variables, there is an increase in risk of breast cancer specific mortality in younger and older patients with ER positive disease, with a substantial increase in risk for women diagnosed before the age of 35. In ER negative disease the risk increases slightly with age. The association between breast cancer specific mortality and both tumour size and number of positive nodes was non-linear with a more marked increase in risk with increasing size and increasing number of nodes in ER positive disease. The overall calibration and discrimination of the new version of PREDICT (v2) was good and comparable to that of the previous version in both model development and validation data sets. However, the calibration of v2 improved over v1 in patients diagnosed under the age of 40. The PREDICT v2 is an improved prognostication and treatment benefit model compared with v1. The online version should continue to aid clinical decision making in women with early breast cancer.

Twitter Demographics

The data shown below were collected from the profiles of 10 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 131 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 <1%
Unknown 130 99%

Demographic breakdown

Readers by professional status Count As %
Researcher 22 17%
Student > Master 22 17%
Student > Ph. D. Student 20 15%
Other 12 9%
Student > Bachelor 7 5%
Other 19 15%
Unknown 29 22%
Readers by discipline Count As %
Medicine and Dentistry 54 41%
Biochemistry, Genetics and Molecular Biology 10 8%
Computer Science 7 5%
Agricultural and Biological Sciences 6 5%
Psychology 5 4%
Other 17 13%
Unknown 32 24%

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 05 June 2017.
All research outputs
#3,959,484
of 16,669,654 outputs
Outputs from Breast Cancer Research
#548
of 1,699 outputs
Outputs of similar age
#73,020
of 273,627 outputs
Outputs of similar age from Breast Cancer Research
#4
of 5 outputs
Altmetric has tracked 16,669,654 research outputs across all sources so far. Compared to these this one has done well and is in the 76th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,699 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.0. 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 273,627 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 73% of its contemporaries.
We're also able to compare this research output to 5 others from the same source and published within six weeks on either side of this one.