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Development and validation of a MEDLINE search filter/hedge for degenerative cervical myelopathy

Overview of attention for article published in BMC Medical Research Methodology, July 2018
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  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (70th percentile)

Mentioned by

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12 tweeters

Citations

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10 Dimensions

Readers on

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18 Mendeley
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Title
Development and validation of a MEDLINE search filter/hedge for degenerative cervical myelopathy
Published in
BMC Medical Research Methodology, July 2018
DOI 10.1186/s12874-018-0529-3
Pubmed ID
Authors

Benjamin M. Davies, Samuel Goh, Keonwoo Yi, Isla Kuhn, Mark R. N. Kotter

Abstract

Degenerative cervical myelopathy (DCM) is a common condition with many unmet clinical needs. Pooled analysis of studies is an important tool for advancing medical understanding. This process starts with a systematic search of the literature. Identification of studies in DCM is challenged by a number of factors, including non-specific terminology and index terms. Search filters or HEDGEs, are search strings developed and validated to optimise medical literature searches. We aimed to develop a search filter for DCM for the MEDLINE database. The diagnostic test assessment framework of a "development dataset" and seperate "validation dataset" was used. The development dataset was formed by hand searching four leading spinal journals (Spine, Journal of Neurosurgery Spine, Spinal Cord and Journal of Spinal Disorders and Techniques) in 2005 and 2010. The search filter was initially developed focusing on sensitivity and subsequently refined using NOT functions to improve specificity. One validation dataset was formed from DCM narrative and systematic review articles and the second, articles published in April of 1989, 1993, 1997, 2001, 2005, 2009, 2013 and 2017 retrieved via the search MeSH term 'Spine'. Metrics of sensitivity, specificity, precision and accuracy were used to test performance. Hand searching identified 77/1094 relevant articles for 2005 and 55/1199 for 2010. We developed a search hedge with 100% sensitivity and a precision of 30 and 29% for the 2005 and 2010 development datasets respectively. For the selected time periods, EXP Spine returned 2113 publications and 30 were considered relevant. The search filter identified all 30 relevant articles, with a specificity of 94% and precision of 20%. Of the 255 references listed in the narrative index reviews, 225 were indexed in MEDLINE and 165 (73%) were relevant articles. All relevant articles were identified and accuracy ranged from 67 to 97% over the three reviews. Of the 42 articles returned from 3 recent systematic reviews, all were identified by the filter. We have developed a highly sensitive hedge for the research of DCM. Whilst precision is similarly low as other hedges, this search filter can be used as an adjunct for DCM search strategies.

Twitter Demographics

The data shown below were collected from the profiles of 12 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 18 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 18 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 5 28%
Librarian 3 17%
Student > Master 2 11%
Professor 1 6%
Lecturer > Senior Lecturer 1 6%
Other 3 17%
Unknown 3 17%
Readers by discipline Count As %
Medicine and Dentistry 7 39%
Social Sciences 3 17%
Nursing and Health Professions 1 6%
Immunology and Microbiology 1 6%
Engineering 1 6%
Other 0 0%
Unknown 5 28%

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 08 November 2018.
All research outputs
#4,460,188
of 18,049,313 outputs
Outputs from BMC Medical Research Methodology
#654
of 1,670 outputs
Outputs of similar age
#83,757
of 285,379 outputs
Outputs of similar age from BMC Medical Research Methodology
#1
of 1 outputs
Altmetric has tracked 18,049,313 research outputs across all sources so far. Compared to these this one has done well and is in the 75th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,670 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.0. This one has gotten more attention than average, scoring higher than 60% 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 285,379 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 70% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them