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Temporal representation of care trajectories of cancer patients using data from a regional information system: an application in breast cancer

Overview of attention for article published in BMC Medical Informatics and Decision Making, April 2014
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (67th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (58th percentile)

Mentioned by

twitter
6 tweeters

Citations

dimensions_citation
21 Dimensions

Readers on

mendeley
58 Mendeley
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Title
Temporal representation of care trajectories of cancer patients using data from a regional information system: an application in breast cancer
Published in
BMC Medical Informatics and Decision Making, April 2014
DOI 10.1186/1472-6947-14-24
Pubmed ID
Authors

Gautier Defossez, Alexandre Rollet, Olivier Dameron, Pierre Ingrand

Abstract

Ensuring that all cancer patients have access to the appropriate treatment within an appropriate time is a strategic priority in many countries. There is in particular a need to describe and analyse cancer care trajectories and to produce waiting time indicators. We developed an algorithm for extracting temporally represented care trajectories from coded information collected routinely by the general cancer Registry in Poitou-Charentes region, France. The present work aimed to assess the performance of this algorithm on real-life patient data in the setting of non-metastatic breast cancer, using measures of similarity.

Twitter Demographics

The data shown below were collected from the profiles of 6 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 58 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 5%
United Kingdom 1 2%
Brazil 1 2%
Canada 1 2%
France 1 2%
Unknown 51 88%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 12 21%
Student > Master 12 21%
Researcher 9 16%
Student > Postgraduate 5 9%
Professor 3 5%
Other 13 22%
Unknown 4 7%
Readers by discipline Count As %
Medicine and Dentistry 13 22%
Computer Science 10 17%
Engineering 9 16%
Nursing and Health Professions 4 7%
Business, Management and Accounting 4 7%
Other 12 21%
Unknown 6 10%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 12 May 2014.
All research outputs
#2,810,146
of 10,451,113 outputs
Outputs from BMC Medical Informatics and Decision Making
#419
of 1,044 outputs
Outputs of similar age
#52,064
of 183,229 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#12
of 29 outputs
Altmetric has tracked 10,451,113 research outputs across all sources so far. This one has received more attention than most of these and is in the 53rd percentile.
So far Altmetric has tracked 1,044 research outputs from this source. They receive a mean Attention Score of 4.8. This one has gotten more attention than average, scoring higher than 56% 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 183,229 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 67% of its contemporaries.
We're also able to compare this research output to 29 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 58% of its contemporaries.