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Can clinical features be used to differentiate type 1 from type 2 diabetes? A systematic review of the literature

Overview of attention for article published in BMJ Open, November 2015
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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 (91st percentile)
  • High Attention Score compared to outputs of the same age and source (83rd percentile)

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2 blogs
policy
1 policy source
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12 X users

Citations

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88 Dimensions

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162 Mendeley
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Title
Can clinical features be used to differentiate type 1 from type 2 diabetes? A systematic review of the literature
Published in
BMJ Open, November 2015
DOI 10.1136/bmjopen-2015-009088
Pubmed ID
Authors

Beverley M Shields, Jaime L Peters, Chris Cooper, Jenny Lowe, Bridget A Knight, Roy J Powell, Angus Jones, Christopher J Hyde, Andrew T Hattersley

Abstract

Clinicians predominantly use clinical features to differentiate type 1 from type 2 diabetes yet there are no evidence-based clinical criteria to aid classification of patients. Misclassification of diabetes is widespread (7-15% of cases), resulting in patients receiving inappropriate treatment. We sought to identify which clinical criteria could be used to discriminate type 1 and type 2 diabetes. Systematic review of all diagnostic accuracy studies published since 1979 using clinical criteria to predict insulin deficiency (measured by C-peptide). 14 databases including: MEDLINE, MEDLINE in Process and EMBASE. The search strategy took the form of: (terms for diabetes) AND (terms for C-Peptide). Diagnostic accuracy studies of any routinely available clinical predictors against a reference standard of insulin deficiency defined by cut-offs of C-peptide concentrations. No restrictions on race, age, language or country of origin. 10 917 abstracts were screened, and 231 full texts reviewed. 11 studies met inclusion criteria, but varied by age, race, year and proportion of participants who were C-peptide negative. Age at diagnosis was the most discriminatory feature in 7/9 studies where it was assessed, with optimal cut-offs (>70% mean sensitivity and specificity) across studies being <30 years or <40 years. Use of/time to insulin treatment and body mass index (BMI) were also discriminatory. When combining features, BMI added little over age at diagnosis and/or time to insulin (<1% improvement in classification). Despite finding only 11 studies, and considerable heterogeneity between studies, age at diagnosis and time to insulin were consistently the most discriminatory criteria. BMI, despite being widely used in clinical practice, adds little to these two criteria. The criteria identified are similar to the Royal College of General Practitioners National Health Service (RCGP/NHS) Diabetes classification guidelines, which use age at diagnosis <35 years and time to insulin <6 m. Until further studies are carried out, these guidelines represent a suitable classification scheme. PROSPERO reference CRD42012001736.

X Demographics

X Demographics

The data shown below were collected from the profiles of 12 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 162 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 2 1%
Hong Kong 1 <1%
Unknown 159 98%

Demographic breakdown

Readers by professional status Count As %
Student > Master 28 17%
Student > Ph. D. Student 20 12%
Researcher 15 9%
Student > Bachelor 15 9%
Student > Postgraduate 14 9%
Other 21 13%
Unknown 49 30%
Readers by discipline Count As %
Medicine and Dentistry 58 36%
Nursing and Health Professions 13 8%
Biochemistry, Genetics and Molecular Biology 8 5%
Agricultural and Biological Sciences 6 4%
Pharmacology, Toxicology and Pharmaceutical Science 4 2%
Other 22 14%
Unknown 51 31%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 22. 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 26 March 2022.
All research outputs
#1,734,918
of 25,374,647 outputs
Outputs from BMJ Open
#3,257
of 25,588 outputs
Outputs of similar age
#25,092
of 296,363 outputs
Outputs of similar age from BMJ Open
#58
of 350 outputs
Altmetric has tracked 25,374,647 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 93rd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 25,588 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 18.2. This one has done well, scoring higher than 87% 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 296,363 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 91% of its contemporaries.
We're also able to compare this research output to 350 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 83% of its contemporaries.