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Machine learning model demonstrates stunting at birth and systemic inflammatory biomarkers as predictors of subsequent infant growth – a four-year prospective study

Overview of attention for article published in BMC Pediatrics, October 2020
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (55th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (51st percentile)

Mentioned by

twitter
5 tweeters

Citations

dimensions_citation
1 Dimensions

Readers on

mendeley
29 Mendeley
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Title
Machine learning model demonstrates stunting at birth and systemic inflammatory biomarkers as predictors of subsequent infant growth – a four-year prospective study
Published in
BMC Pediatrics, October 2020
DOI 10.1186/s12887-020-02392-3
Pubmed ID
Authors

Elizabeth Harrison, Sana Syed, Lubaina Ehsan, Najeeha T. Iqbal, Kamran Sadiq, Fayyaz Umrani, Sheraz Ahmed, Najeeb Rahman, Sadaf Jakhro, Jennie Z. Ma, Molly Hughes, S. Asad Ali

Twitter Demographics

The data shown below were collected from the profiles of 5 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 29 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 29 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 21%
Student > Bachelor 4 14%
Student > Master 2 7%
Other 2 7%
Lecturer > Senior Lecturer 1 3%
Other 4 14%
Unknown 10 34%
Readers by discipline Count As %
Agricultural and Biological Sciences 4 14%
Medicine and Dentistry 4 14%
Nursing and Health Professions 2 7%
Social Sciences 2 7%
Computer Science 1 3%
Other 1 3%
Unknown 15 52%

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 02 November 2020.
All research outputs
#9,462,849
of 17,601,811 outputs
Outputs from BMC Pediatrics
#1,158
of 2,254 outputs
Outputs of similar age
#170,327
of 386,177 outputs
Outputs of similar age from BMC Pediatrics
#144
of 304 outputs
Altmetric has tracked 17,601,811 research outputs across all sources so far. This one is in the 45th percentile – i.e., 45% of other outputs scored the same or lower than it.
So far Altmetric has tracked 2,254 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.1. This one is in the 48th percentile – i.e., 48% of its peers scored the same or lower than it.
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 386,177 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 55% of its contemporaries.
We're also able to compare this research output to 304 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 51% of its contemporaries.