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Prediction models for the development of COPD: a systematic review

Overview of attention for article published in International Journal of Chronic Obstructive Pulmonary Disease, June 2018
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (89th percentile)
  • High Attention Score compared to outputs of the same age and source (98th percentile)

Mentioned by

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2 news outlets
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9 X users

Citations

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24 Dimensions

Readers on

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74 Mendeley
Title
Prediction models for the development of COPD: a systematic review
Published in
International Journal of Chronic Obstructive Pulmonary Disease, June 2018
DOI 10.2147/copd.s155675
Pubmed ID
Authors

Melanie C Matheson, Gayan Bowatte, Jennifer L Perret, Adrian J Lowe, Chamara V Senaratna, Graham L Hall, Nick de Klerk, Louise A Keogh, Christine F McDonald, Nilakshi T Waidyatillake, Peter D Sly, Deborah Jarvis, Michael J Abramson, Caroline J Lodge, Shyamali C Dharmage

Abstract

Early identification of people at risk of developing COPD is crucial for implementing preventive strategies. We aimed to systematically review and assess the performance of all published models that predicted development of COPD. A search was conducted to identify studies that developed a prediction model for COPD development. The Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies was followed when extracting data and appraising the selected studies. Of the 4,481 records identified, 30 articles were selected for full-text review, and only four of these were eligible to be included in the review. The only consistent predictor across all four models was a measure of smoking. Sex and age were used in most models; however, other factors varied widely. Two of the models had good ability to discriminate between people who were correctly or incorrectly classified as at risk of developing COPD. Overall none of the models were particularly useful in accurately predicting future risk of COPD, nor were they good at ruling out future risk of COPD. Further studies are needed to develop new prediction models and robustly validate them in external cohorts.

X Demographics

X Demographics

The data shown below were collected from the profiles of 9 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 74 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 74 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 9 12%
Student > Master 8 11%
Student > Bachelor 8 11%
Student > Doctoral Student 5 7%
Student > Postgraduate 4 5%
Other 13 18%
Unknown 27 36%
Readers by discipline Count As %
Medicine and Dentistry 18 24%
Nursing and Health Professions 6 8%
Agricultural and Biological Sciences 3 4%
Social Sciences 3 4%
Biochemistry, Genetics and Molecular Biology 2 3%
Other 12 16%
Unknown 30 41%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 21. 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 15 June 2021.
All research outputs
#1,776,893
of 25,382,440 outputs
Outputs from International Journal of Chronic Obstructive Pulmonary Disease
#125
of 2,578 outputs
Outputs of similar age
#36,963
of 342,877 outputs
Outputs of similar age from International Journal of Chronic Obstructive Pulmonary Disease
#1
of 77 outputs
Altmetric has tracked 25,382,440 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,578 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.5. This one has done particularly well, scoring higher than 95% 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 342,877 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 89% of its contemporaries.
We're also able to compare this research output to 77 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 98% of its contemporaries.