Evidence map›Paper›PMID 36942014›Full record

ArticleAJPM focus2022

Patient Self-Report Superior to Electronic Medical Record Abstraction for Identifying Positive COVID-19 Symptoms at Illness Onset.

Gretchen L Mockler, Samantha P Novotny, Wei Hou, Yuhang Liu, Elinor R Schoenfeld

Erratum issuedOpen access · goldAbstract read
In one paragraph

Article in AJPM focus, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.2field-weighted citation impact, top 51% of its field
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

2 citing papers in PubMed, 3 citations in OpenAlex.

  1. Article
  2. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors at 2 institutions in 1 country.

Gretchen L MocklerDepartment of Family, Population & Preventive Medicine, Renaissance School of Medicine, Stony Brook University Hospital, Stony Brook, New York.
Samantha P NovotnyRenaissance School of Medicine, Stony Brook University Hospital, Stony Brook, New York.
Wei HouDepartment of Family, Population & Preventive Medicine, Renaissance School of Medicine, Stony Brook University Hospital, Stony Brook, New York.
Yuhang LiuDepartment of Applied Mathematics & Statistics, College of Engineering and Applied Sciences, Stony Brook University, Stony Brook, New York.
Elinor R SchoenfeldDepartment of Family, Population & Preventive Medicine, Renaissance School of Medicine, Stony Brook University Hospital, Stony Brook, New York.
Stony Brook University Hospital · USStony Brook University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Most initial COVID-19 research focused on hospitalized patients. Presenting symptomatology in the outpatient setting was poorly characterized, making it difficult for primary care physicians to predict which patients would require hospitalization. The purpose of this study was to characterize the presenting symptoms of COVID-19 infection and baseline patient characteristics and evaluate for correlation with disease severity, duration, and chronicity in the outpatient setting. Methods: A total of 107 adult, English-speaking patients with suspected and confirmed COVID-19 cases at the 3 primary care practices of Stony Brook University Hospital were studied between March and December 2020. Survey data were collected from patient telephone interviews and electronic medical record abstraction. The potential risk factors assessed included participant demographics, medical comorbidities, and the number and type of symptoms at illness onset. Outcome measures included symptom duration, hospitalizations, and persistence of symptoms at 12 weeks from study enrollment Results: Patient self-report survey elicited nearly twice as many symptoms described at illness onset as those recorded in the electronic medical record ( Conclusions: Early in the setting of newly emerging infectious diseases, particularly those such as COVID-19 that involve multiple organ systems, patient self-report of symptoms using a complete review of systems rather than electronic medical record abstraction alone may be key for accurate disease identification and characterization as well as prediction of eventual disease severity, duration, and chronicity.

Indexed as

COVID-19hospitalizationSARS-CoV-2

Identifiers

PMID36942014
PMCPMC9095498
OpenAlexW4280538217

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

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.