↓ Skip to main content

Characteristics of undiagnosed diseases network applicants: implications for referring providers

Overview of attention for article published in BMC Health Services Research, August 2018
Altmetric Badge

About this Attention Score

  • Average Attention Score compared to outputs of the same age

Mentioned by

3 tweeters


1 Dimensions

Readers on

23 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Characteristics of undiagnosed diseases network applicants: implications for referring providers
Published in
BMC Health Services Research, August 2018
DOI 10.1186/s12913-018-3458-2
Pubmed ID

Nicole M. Walley, Loren D. M. Pena, Stephen R. Hooper, Heidi Cope, Yong-Hui Jiang, Allyn McConkie-Rosell, Camilla Sanders, Kelly Schoch, Rebecca C. Spillmann, Kimberly Strong, Alexa T. McCray, Paul Mazur, Cecilia Esteves, Kimberly LeBlanc, Anastasia L. Wise, Vandana Shashi


The majority of undiagnosed diseases manifest with objective findings that warrant further investigation. The Undiagnosed Diseases Network (UDN) receives applications from patients whose symptoms and signs have been intractable to diagnosis; however, many UDN applicants are affected primarily by subjective symptoms such as pain and fatigue. We sought to characterize presenting symptoms, referral sources, and demographic factors of applicants to the UDN to identify factors that may determine application outcome and potentially differentiate between those with undiagnosed diseases (with more objective findings) and those who are less likely to have an undiagnosed disease (more subjective symptoms). We used a systematic retrospective review of 151 consecutive Not Accepted and 50 randomly selected Accepted UDN applications. The primary outcome was whether an applicant was Accepted, or Not Accepted, and, if accepted, whether or not a diagnosis was made. Objective and subjective symptoms and information on prior specialty consultations were collected from provider referral letters. Demographic data and decision data on network acceptance were gathered from the UDN online portal. Fewer objective findings and more subjective symptoms were found in the Not Accepted applications. Not Accepted referrals also were from older individuals, reported a shorter period of illness, and were referred to the UDN by their primary care physicians. All of these differences reached statistical significance in comparison with Accepted applications. The frequency of subspecialty consults for diagnostic purposes prior to UDN application was similar in both groups. The preponderance of subjective and lack of objective findings in the Not Accepted applications distinguish these from applicants that are accepted for evaluation and diagnostic efforts through the UDN. Not Accepted applicants are referred primarily by their primary care providers after multiple specialist consultations fail to yield answers. Distinguishing between patients with undiagnosed diseases with objective findings and those with primarily subjective findings can delineate patients who would benefit from further diagnostic processes from those who may have functional disorders and need alternative pathways for management of their symptoms. clinicaltrials.gov NCT02450851 , posted May 21st 2015.

Twitter Demographics

The data shown below were collected from the profiles of 3 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 23 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 23 100%

Demographic breakdown

Readers by professional status Count As %
Unspecified 6 26%
Professor 4 17%
Student > Master 3 13%
Student > Doctoral Student 3 13%
Student > Ph. D. Student 2 9%
Other 5 22%
Readers by discipline Count As %
Medicine and Dentistry 6 26%
Unspecified 5 22%
Biochemistry, Genetics and Molecular Biology 4 17%
Nursing and Health Professions 2 9%
Psychology 2 9%
Other 4 17%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 29 August 2018.
All research outputs
of 13,441,462 outputs
Outputs from BMC Health Services Research
of 4,492 outputs
Outputs of similar age
of 266,595 outputs
Outputs of similar age from BMC Health Services Research
of 2 outputs
Altmetric has tracked 13,441,462 research outputs across all sources so far. This one is in the 40th percentile – i.e., 40% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,492 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.5. This one is in the 33rd percentile – i.e., 33% 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 266,595 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 45th percentile – i.e., 45% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 2 others from the same source and published within six weeks on either side of this one.