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Attention Score in Context
| Title |
A systematic review of predictive models for asthma development in children
|
|---|---|
| Published in |
BMC Medical Informatics and Decision Making, November 2015
|
| DOI | 10.1186/s12911-015-0224-9 |
| Pubmed ID | |
| Authors |
Gang Luo, Flory L. Nkoy, Bryan L. Stone, Darell Schmick, Michael D. Johnson |
| Abstract |
Asthma is the most common pediatric chronic disease affecting 9.6 % of American children. Delay in asthma diagnosis is prevalent, resulting in suboptimal asthma management. To help avoid delay in asthma diagnosis and advance asthma prevention research, researchers have proposed various models to predict asthma development in children. This paper reviews these models. A systematic review was conducted through searching in PubMed, EMBASE, CINAHL, Scopus, the Cochrane Library, the ACM Digital Library, IEEE Xplore, and OpenGrey up to June 3, 2015. The literature on predictive models for asthma development in children was retrieved, with search results limited to human subjects and children (birth to 18 years). Two independent reviewers screened the literature, performed data extraction, and assessed article quality. The literature search returned 13,101 references in total. After manual review, 32 of these references were determined to be relevant and are discussed in the paper. We identify several limitations of existing predictive models for asthma development in children, and provide preliminary thoughts on how to address these limitations. Existing predictive models for asthma development in children have inadequate accuracy. Efforts to improve these models' performance are needed, but are limited by a lack of a gold standard for asthma development in children. |
X Demographics
The data shown below were collected from the profiles of 10 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Spain | 4 | 40% |
| India | 1 | 10% |
| United States | 1 | 10% |
| Unknown | 4 | 40% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 7 | 70% |
| Practitioners (doctors, other healthcare professionals) | 2 | 20% |
| Scientists | 1 | 10% |
Mendeley readers
The data shown below were compiled from readership statistics for 36 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 36 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Bachelor | 5 | 14% |
| Student > Ph. D. Student | 4 | 11% |
| Other | 3 | 8% |
| Student > Master | 3 | 8% |
| Researcher | 2 | 6% |
| Other | 5 | 14% |
| Unknown | 14 | 39% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 11 | 31% |
| Computer Science | 3 | 8% |
| Nursing and Health Professions | 2 | 6% |
| Unspecified | 1 | 3% |
| Business, Management and Accounting | 1 | 3% |
| Other | 1 | 3% |
| Unknown | 17 | 47% |
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 06 May 2016.
All research outputs
#6,009,743
of 23,848,132 outputs
Outputs from BMC Medical Informatics and Decision Making
#519
of 2,045 outputs
Outputs of similar age
#89,766
of 394,272 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#10
of 37 outputs
Altmetric has tracked 23,848,132 research outputs across all sources so far. This one has received more attention than most of these and is in the 74th percentile.
So far Altmetric has tracked 2,045 research outputs from this source. They receive a mean Attention Score of 5.0. This one has gotten more attention than average, scoring higher than 74% 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 394,272 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 77% of its contemporaries.
We're also able to compare this research output to 37 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 75% of its contemporaries.