Evidence map›Paper›PMID 29950461›Full record

ArticleBMJ open2018

Derivation and validation of a chief complaint shortlist for unscheduled acute and emergency care in Uganda.

Brian Travis Rice, Mark Bisanzo, Samuel Maling, Ryan Joseph, Hani Mowafi, Global Emergency Care Investigators Group (Study Group)

Abstract readValidation Study
In one paragraph

Article in BMJ open, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–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

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Defining High-risk Emergency Chief Complaints: Data-driven Triage for Low- and Middle-income Countries.Academic emergency medicine : official journal of the Society for Academic Emergency Medicine · 2020
    Article
  5. Article
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

6 authors.

Brian Travis RiceEmergency Medicine, New York University Langone Medical Center, New York City, New York, USA.ORCID 0000-0002-9093-1831
Mark BisanzoDivision of Emergency Medicine, Department of Surgery, University of Vermont, Burlington, Vermont, USA.
Samuel MalingPsychiatry, Mbarara University of Science and Technology, Mbarara, Uganda.
Ryan JosephEmergency Medicine, Texas A&M, Corpus Christi, Texas, USA.
Hani MowafiEmergency Medicine, Yale University, New Haven, Connecticut, USA.
Global Emergency Care Investigators Group (Study Group)

Funding

Yale Clinical and Translational Science Award (U Component)UL1TR001863 · NCATS · YALE UNIVERSITY · PI John H. Krystal, LUCILA OHNO-MACHADO · 2016 to 2026
$102.9M
6 · The paper itself

Abstract

objectivesDerive and validate a shortlist of chief complaints to describe unscheduled acute and emergency care in Uganda.

settingA single, private, not-for profit hospital in rural, southwestern Uganda.

participantsFrom 2009 to 2015, 26 996 patient visits produced 42 566 total chief complaints for the derivation dataset, and from 2015 to 2017, 10 068 visits produced 20 165 total chief complaints for the validation dataset.

methodsA retrospective review of an emergency centre quality assurance database was performed. Data were abstracted, cleaned and refined using language processing in Stata to produce a longlist of chief complaints, which was collapsed via a consensus process to produce a shortlist and turned into a web-based tool. This tool was used by two local Ugandan emergency care practitioners to categorise complaints from a second longlist produced from a separate validation dataset from the same study site. Their agreement on grouping was analysed using Cohen's kappa to determine inter-rater reliability. The chief complaints describing 80% of patient visits from automated and consensus shortlists were combined to form a candidate chief complaint shortlist.

resultsAutomated data cleaning and refining recognised 95.8% of all complaints and produced a longlist of 555 chief complaints. The consensus process yielded a shortlist of 83 grouped chief complaints. The second validation dataset was reduced in Stata to a longlist of 451 complaints. Using the shortlist tool to categorise complaints produced 71.5% agreement, yielding a kappa of 0.70 showing substantial inter-rater reliability. Only one complaint did not fit into the shortlist and required a free-text amendment. The two shortlists were identical for the most common 14 complaints and combined to form a candidate list of 24 complaints that could characterise over 80% of all emergency centre chief complaints.

conclusionsShortlists of chief complaints can be generated to improve standardisation of data entry, facilitate research efforts and be employed for paper chart usage.

Indexed as

AdolescentAdultAgedAged, 80 and overChildChild, PreschoolEmergency Medical ServicesEmergency Service, HospitalFemaleHumansInfantInfant, NewbornInternational Classification of DiseasesMaleMiddle AgedPatient Dischargeepidemiologypublic health

Identifiers

PMID29950461
PMCPMC6020949

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Registered trials

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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.