Evidence map›Paper›PMID 35387814›Full record

ArticleBMJ open2022

Predicting diagnoses from illness experiences with common cold symptoms before physician consultation: a mixed-methods research.

Atsushi Mizoe, Naohi Isse

Open access · goldAbstract read
In one paragraph

Article in BMJ open, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
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0citing papers in PubMed
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1 · What the graph read from it

What it found

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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

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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

0 citing papers in PubMed, 0 citations in OpenAlex.

No citing paper in PubMed yet.

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

2 authors at 1 institution in 1 country.

Atsushi MizoeCommunity Based Medicine, Fujita Health University, Toyoake, Aichi, Japan.
Naohi IsseGeneral Medicine, Ako City Hospital, Ako, Hyogo, Japan issenaohi@yahoo.co.jp.ORCID 0000-0001-5283-1592
Fujita Health University · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesWe aimed to explore whether patients' illness experiences with common cold symptoms could help with predicting the final diagnosis before consultation.

designExploratory sequential design of mixed methods: Qualitative and quantitative studies used inductive qualitative content analysis and multinomial regression analysis, respectively.

settingConsecutive patients at the primary care clinic of a general hospital.

participantsNew patients aged 15 years or older were included in the study. Of the 1512 eligible patients who received the questionnaire sheet, 408 selected the common cold as their reason for visiting. All 408 patients responded to the questionnaire.

main outcome measuresFirst, factors representing illness experiences in patients with common cold symptoms were explored. Second, variables with significant relative risk ratio (RRR) were used to diagnose common cold, influenza or other diseases.

resultsA total of 171 codes were identified from the responses of 408 patients, which were visually mapped to show their frequencies and occurrence in the same person according to their final diagnoses. Of the 171 codes, 22 found in over nine patients represented the variables for the three independent final diagnoses. The adjusted final model revealed that (1) 'worry about influenza infection', 'want influenza test' and 'transmission from a colleague at school or workplace' predicted the influenza rather than the common cold, when other predicting variables were constant (RRR, 6.20 p<0.001; RRR, 26.1 p<0.01; and RRR, 4.69 p<0.05, respectively); (2) 'want further examination' predicted other diseases (RRR, 2.84 p<0.05); and (3) the combination of 'worry about influenza infection' and 'want influenza test', which predicted the opposite diagnosis: the common cold rather than influenza (RRR, 0.01 p<0.001).

conclusionThese findings provide useful information on how illness experiences before consultation can predict final diagnoses for patients with common cold symptoms. TRIAL REGISTRATION NUMBER: UMIN000030697.

Indexed as

Common ColdInfluenza, HumanPhysiciansAdolescentHumansReferral and ConsultationSurveys and Questionnairesgeneral medicine (see internal medicine)primary carequalitative researchrespiratory infections

Identifiers

PMID35387814
PMCPMC8987796
OpenAlexW4223543107

What OpenQuestion holds

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LicenceCC BY-NC
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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.