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Improving the primary care physicians' decision making for fibromyalgia in clinical practice: development and validation of the Fibromyalgia Detection (FibroDetect®) screening tool

Overview of attention for article published in Health and Quality of Life Outcomes, October 2014
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Title
Improving the primary care physicians' decision making for fibromyalgia in clinical practice: development and validation of the Fibromyalgia Detection (FibroDetect®) screening tool
Published in
Health and Quality of Life Outcomes, October 2014
DOI 10.1186/s12955-014-0128-x
Pubmed ID
Authors

Ralf Baron, Serge Perrot, Isabelle Guillemin, Cayetano Alegre, Carla Dias-Barbosa, Ernest Choy, Héléne Gilet, Giorgio Cruccu, Jules Desmeules, Joëlle Margaux, Selwyn Richards, Eric Serra, Michael Spaeth, Benoit Arnould

Abstract

BackgroundFibromyalgia diagnosis is a challenging and long process, especially among primary care physicians (PCPs), because of symptom heterogeneity, co-morbidities and clinical overlap with other disorders. The purpose was to develop and validate a screening tool in French (FR), German (DE) and English (UK) to help primary care physicians (PCPs) identify patients with fibromyalgia.MethodsThe FibroDetect questionnaire was simultaneously developed in FR, DE and UK based on information obtained from a literature review, focus groups conducted with clinicians, and face-to-face interviews with fibromyalgia patients (FR, DE and UK, n¿=¿23). The resulting tool was comprehension-tested in patients with diagnosed or suspected fibromyalgia (n¿=¿3 and n¿=¿2 in each country, respectively). Acceptability and applicability were assessed and the tool modified accordingly, then assessed in clinical practice. A scoring method was created using an iterative process based on statistical and clinical considerations with American College of Rheumatology¿+¿(ACR+) patients and ACR¿ patients (n¿=¿276), and validated with fibromyalgia and non-fibromyalgia patients (n¿=¿312).ResultsThe FibroDetect included 14 questions assessing patients¿ pain and fatigue, personal history and attitudes, symptoms and impact on lives. Six questions were retained in the final scoring, demonstrating satisfactory discriminative power between ACR¿+¿and ACR- patients with area under the Receiver Operating Characteristic curve of 0.74. The predictive accuracy of the tool increased to 0.86 for fibromyalgia and non-fibromyalgia patient detection, with a sensitivity of 90% and a specificity of 67% for a cut-off of 6 on the score.ConclusionsThe FibroDetect is a self-administered tool that can be used as a screening classification surrogate to the ACR criteria in primary care settings to help PCPs detect potential fibromyalgia patients among a population complaining of chronic widespread pain.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 98 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 2 2%
Spain 1 1%
Austria 1 1%
Unknown 94 96%

Demographic breakdown

Readers by professional status Count As %
Other 12 12%
Student > Postgraduate 11 11%
Student > Bachelor 10 10%
Student > Doctoral Student 9 9%
Student > Master 9 9%
Other 23 23%
Unknown 24 24%
Readers by discipline Count As %
Medicine and Dentistry 39 40%
Nursing and Health Professions 10 10%
Psychology 6 6%
Neuroscience 4 4%
Social Sciences 4 4%
Other 10 10%
Unknown 25 26%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 12 November 2014.
All research outputs
#15,309,583
of 22,769,322 outputs
Outputs from Health and Quality of Life Outcomes
#1,302
of 2,158 outputs
Outputs of similar age
#151,982
of 260,971 outputs
Outputs of similar age from Health and Quality of Life Outcomes
#11
of 18 outputs
Altmetric has tracked 22,769,322 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 2,158 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 32nd percentile – i.e., 32% 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 260,971 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 32nd percentile – i.e., 32% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 18 others from the same source and published within six weeks on either side of this one. This one is in the 22nd percentile – i.e., 22% of its contemporaries scored the same or lower than it.