Evidence map›Paper›PMID 29020951›Full record

ArticleBMC medical informatics and decision making2017

Paramedic literature search filters: optimised for clinicians and academics.

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

Abstract read
In one paragraph

Article in BMC medical informatics and decision making, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
26citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

26 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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  17. Validation of a search strategy for randomized clinical trials related to periodontitis.Archives of public health = Archives belges de sante publique · 2021
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  18. The Definition of Paramedicine: An International Delphi Study.Journal of multidisciplinary healthcare · 2021
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Alexander OlaussenDepartment of Community Emergency Health and Paramedic Practice, Monash University, Melbourne, Australia. alexander.olaussen@monash.edu.ORCID http://orcid.org/0000-0001-8799-077X
William SempleDepartment of Community Emergency Health and Paramedic Practice, Monash University, Melbourne, Australia.
Alaa OteirDepartment of Community Emergency Health and Paramedic Practice, Monash University, Melbourne, Australia.
Paula ToddDepartment of Community Emergency Health and Paramedic Practice, Monash University, Melbourne, Australia.
Brett WilliamsDepartment of Community Emergency Health and Paramedic Practice, Monash University, Melbourne, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSearch 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.

methodsWe 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).

resultsWe 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.

conclusionsWe 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.

Indexed as

Allied Health PersonnelInformation ServicesInformation Storage and RetrievalHumansSensitivity and SpecificityParamedicSearch filter

Identifiers

PMID29020951
PMCPMC5637081

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.