Paramedic literature search filters

Optimised for clinicians and academics

Alexander Olaussen, William Semple, Alaa Oteir, Paula Todd, Brett Williams

Research output: Contribution to journalArticleResearchpeer-review

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.

Original languageEnglish
Article number146
Number of pages6
JournalBMC Medical Informatics and Decision Making
Volume17
Issue number1
DOIs
Publication statusPublished - 11 Oct 2017

Keywords

  • Paramedic
  • Search filter

Cite this

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title = "Paramedic literature search filters: Optimised for clinicians and academics",
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",
author = "Alexander Olaussen and William Semple and Alaa Oteir and Paula Todd and Brett Williams",
year = "2017",
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language = "English",
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journal = "BMC Medical Informatics and Decision Making",
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Paramedic literature search filters : Optimised for clinicians and academics. / Olaussen, Alexander; Semple, William; Oteir, Alaa; Todd, Paula; Williams, Brett.

In: BMC Medical Informatics and Decision Making, Vol. 17, No. 1, 146, 11.10.2017.

Research output: Contribution to journalArticleResearchpeer-review

TY - JOUR

T1 - Paramedic literature search filters

T2 - Optimised for clinicians and academics

AU - Olaussen, Alexander

AU - Semple, William

AU - Oteir, Alaa

AU - Todd, Paula

AU - Williams, Brett

PY - 2017/10/11

Y1 - 2017/10/11

N2 - 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.

AB - 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.

KW - Paramedic

KW - Search filter

UR - http://www.scopus.com/inward/record.url?scp=85031014968&partnerID=8YFLogxK

U2 - 10.1186/s12911-017-0544-z

DO - 10.1186/s12911-017-0544-z

M3 - Article

VL - 17

JO - BMC Medical Informatics and Decision Making

JF - BMC Medical Informatics and Decision Making

SN - 1472-6947

IS - 1

M1 - 146

ER -