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BMC Med Inform Decis Mak. 2017 Oct 11;17(1):146. doi: 10.1186/s12911-017-0544-z.

Paramedic literature search filters: optimised for clinicians and academics.

Olaussen A1,2,3,4,5, Semple W6, Oteir A6,7, Todd P6,8, Williams B6,9.

Author information

1
Department of Community Emergency Health and Paramedic Practice, Monash University, Melbourne, Australia. alexander.olaussen@monash.edu.
2
Emergency & Trauma Centre, The Alfred Hospital, Melbourne, Australia. alexander.olaussen@monash.edu.
3
Trauma Service, The Alfred Hospital, Melbourne, Australia. alexander.olaussen@monash.edu.
4
National Trauma Research Institute, The Alfred Hospital, Melbourne, Australia. alexander.olaussen@monash.edu.
5
Department of Community Emergency Health & Paramedic Practice, Monash University, Peninsula Campus, PO Box 527, McMahons Road, Frankston, VIC, 3199, Australia. alexander.olaussen@monash.edu.
6
Department of Community Emergency Health and Paramedic Practice, Monash University, Melbourne, Australia.
7
Paramedic Program, Department of Allied Medical Sciences, Jordan University of Science and Technology, Irbid, Jordan.
8
Monash University Library, Melbourne, Australia.
9
Division of Paramedicine, School of Medicine, University of Tasmania, Hobart, Australia.

Abstract

BACKGROUND:

Search filters aid clinicians and academics to accurately locate literature. Despite this, there is no search filter or Medical Subject Headings (MeSH) term pertaining to paramedics. Therefore, the aim of this study was to create two filters to meet to different needs of paramedic clinicians and academics.

METHODS:

We created a gold standard from a reference set, which we measured against single terms and search filters. The words and phrases used stemmed from selective exclusion of terms from the previously published Prehospital Search Filter 2.0 as well as a Delphi session with an expert panel of paramedic researchers. Independent authors deemed articles paramedic-relevant or not following an agreed definition. We measured sensitivity, specificity, accuracy and number needed to read (NNR).

RESULTS:

We located 2102 articles of which 431 (20.5%) related to paramedics. The performance of single terms was on average of high specificity (97.1% (Standard Deviation 7.4%), but of poor sensitivity (12.0%, SD 18.7%). The NNR ranged from 1 to 8.6. The sensitivity-maximising search filter yielded 98.4% sensitivity, with a specificity of 74.3% and a NNR of 2. The specificity-maximising filter achieved 88.3% in specificity, which only lowered the sensitivity to 94.7%, and thus a NNR of 1.48.

CONCLUSIONS:

We have created the first two paramedic specific search filters, one optimised for sensitivity and one optimised for specificity. The sensitivity-maximising search filter yielded 98.4% sensitivity, and a NNR of 2. The specificity-maximising filter achieved 88.3% in specificity, which only lowered the sensitivity to 94.7%, and a NNR of 1.48. A paramedic MeSH term is needed.

KEYWORDS:

Paramedic; Search filter

PMID:
29020951
PMCID:
PMC5637081
DOI:
10.1186/s12911-017-0544-z
[Indexed for MEDLINE]
Free PMC Article

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