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“Medically unexplained” symptoms and symptom disorders in primary care: prognosis-based recognition and classification

Overview of attention for article published in BMC Primary Care, February 2017
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (88th percentile)
  • Good Attention Score compared to outputs of the same age and source (78th percentile)

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1 blog
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16 X users
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1 Facebook page

Citations

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82 Dimensions

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183 Mendeley
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Title
“Medically unexplained” symptoms and symptom disorders in primary care: prognosis-based recognition and classification
Published in
BMC Primary Care, February 2017
DOI 10.1186/s12875-017-0592-6
Pubmed ID
Authors

Marianne Rosendal, Tim C Olde Hartman, Aase Aamland, Henriette van der Horst, Peter Lucassen, Anna Budtz-Lilly, Christopher Burton

Abstract

Many patients consult their GP because they experience bodily symptoms. In a substantial proportion of cases, the clinical picture does not meet the existing diagnostic criteria for diseases or disorders. This may be because symptoms are recent and evolving or because symptoms are persistent but, either by their character or the negative results of clinical investigation cannot be attributed to disease: so-called "medically unexplained symptoms" (MUS). MUS are inconsistently recognised, diagnosed and managed in primary care. The specialist classification systems for MUS pose several problems in a primary care setting. The systems generally require great certainty about presence or absence of physical disease, they tend to be mind-body dualistic, and they view symptoms from a narrow specialty determined perspective. We need a new classification of MUS in primary care; a classification that better supports clinical decision-making, creates clearer communication and provides scientific underpinning of research to ensure effective interventions. We propose a classification of symptoms that places greater emphasis on prognostic factors. Prognosis-based classification aims to categorise the patient's risk of ongoing symptoms, complications, increased healthcare use or disability because of the symptoms. Current evidence suggests several factors which may be used: symptom characteristics such as: number, multi-system pattern, frequency, severity. Other factors are: concurrent mental disorders, psychological features and demographic data. We discuss how these characteristics may be used to classify symptoms into three groups: self-limiting symptoms, recurrent and persistent symptoms, and symptom disorders. The middle group is especially relevant in primary care; as these patients generally have reduced quality of life but often go unrecognised and are at risk of iatrogenic harm. The presented characteristics do not contain immediately obvious cut-points, and the assessment of prognosis depends on a combination of several factors. Three criteria (multiple symptoms, multiple systems, multiple times) may support the classification into good, intermediate and poor prognosis when dealing with symptoms in primary care. The proposed new classification specifically targets the patient population in primary care and may provide a rational framework for decision-making in clinical practice and for epidemiologic and clinical research of symptoms.

X Demographics

X Demographics

The data shown below were collected from the profiles of 16 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 183 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 <1%
Portugal 1 <1%
Unknown 181 99%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 26 14%
Researcher 22 12%
Student > Master 21 11%
Student > Ph. D. Student 19 10%
Other 16 9%
Other 38 21%
Unknown 41 22%
Readers by discipline Count As %
Medicine and Dentistry 59 32%
Psychology 31 17%
Nursing and Health Professions 11 6%
Neuroscience 5 3%
Agricultural and Biological Sciences 4 2%
Other 24 13%
Unknown 49 27%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 15. 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 17 September 2022.
All research outputs
#2,330,762
of 25,382,440 outputs
Outputs from BMC Primary Care
#267
of 2,359 outputs
Outputs of similar age
#47,508
of 424,567 outputs
Outputs of similar age from BMC Primary Care
#9
of 42 outputs
Altmetric has tracked 25,382,440 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 90th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,359 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.7. This one has done well, scoring higher than 88% 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 424,567 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 88% of its contemporaries.
We're also able to compare this research output to 42 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 78% of its contemporaries.