Evidence map›Paper›PMID 32743605›Full record

ArticlemedRxiv : the preprint server for health sciences2020

The best COVID-19 predictor is recent smell loss: a cross-sectional study.

Richard C Gerkin, Kathrin Ohla, Maria Geraldine Veldhuizen, Paule V Joseph, Christine E Kelly, Alyssa J Bakke, Kimberley E Steele, Michael C Farruggia, Robert Pellegrino, Marta Y Pepino and 119 more

Open access · greenAbstract readPreprint
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed, 37 citations in OpenAlex.

  1. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

129 authors at 20 institutions in 33 countries.

Maria Geraldine VeldhuizenORCID 0000-0001-8375-7719
Christine E Kelly
Kimberley E SteeleORCID 0000-0001-5096-6549
Michael C FarruggiaORCID 0000-0002-5306-6754
Robert PellegrinoORCID 0000-0002-5319-828X
Graciela M Soler
Veronica Pereda-LothORCID 0000-0002-7365-6217
Michele DibattistaORCID 0000-0002-3955-4663
Antonella Di PizioORCID 0000-0002-8520-5165
Alexander W FjaeldstadORCID 0000-0002-9135-6920
Preet B Singh
Mohammed K AlwashahiORCID 0000-0003-2704-5527
Surabhi Bhutani
Anna D'Errico
Marco A FornazieriORCID 0000-0001-5213-2337
Jérôme GolebiowskiORCID 0000-0002-3675-1952
Katherine L WhitcroftORCID 0000-0002-1417-0049
Florian Ph S FischmeisterORCID 0000-0003-4573-7666
Thomas HeinbockelORCID 0000-0003-4347-2131
Iordanis Konstantinidis
Vonnie D C ShieldsORCID 0000-0001-9836-4416
Vera V Voznessenskaya
María Adelaida Bock
Orietta CalcinoniORCID 0000-0003-4665-3106
William Fredborg
Christophe Laudamiel
Johan N LundströmORCID 0000-0002-3529-8981
Alberto Macchi
Enrique SantamaríaORCID 0000-0001-8046-8102
Paloma Paloma DomínguezORCID 0000-0002-5242-9947
Sanne Boesveldt
Jasper H B de GrootORCID 0000-0002-2831-3557
Caterina DinnellaORCID 0000-0002-5765-6057
Tatiana Laktionova
Sajidxa Mariño
Erminio Monteleone
Alexia Nunez-Parra
Olagunju AbdulrahmanORCID 0000-0001-9863-7912
Thierry Thomas-DanguinORCID 0000-0002-9756-355X
Julie Walsh-MessingerORCID 0000-0002-7641-9321
Emmanuelle BignonORCID 0000-0001-9475-5049
Maria Paola Cecchini
Maria Dolors GuàrdiaORCID 0000-0001-7358-9467
Noam Karni
Patricia Portillo MazalORCID 0000-0002-5260-8849
Atiye Sarabi-Jamab
Nicholas S ArcherORCID 0000-0003-3612-2950
Ben Chen
Elizabeth A Di ValerioORCID 0000-0002-1762-3453
Johannes FrasnelliORCID 0000-0002-8863-3982
Yuping Ning
Jeb M Justice
Pavan Kumar KaushikORCID 0000-0002-9431-3993
Sachiko Koyama
Jonathan B OverdevestORCID 0000-0002-1152-2512
Nicola Pirastu
Vicente A RamirezORCID 0000-0001-5005-745X
Hongyuan Cao
Hong Wang
Valentina Parma
Monell Chemical Senses Center · USUniversity of Illinois Urbana-Champaign · USUniversity of Padua · ITHebrew University of Jerusalem · ILIndraprastha Institute of Information Technology Delhi · INNational Institutes of Health · USPennsylvania State University · USStockholm University · SEUC San Diego Health System · USUniversidad de Buenos Aires · ARUtrecht University · NLYale University · USAgriculture and Forestry University · NPArak University · IRArizona State University · USCliniques Universitaires Saint-Luc · BEColumbia University Irving Medical Center · USCommonwealth Scientific and Industrial Research Organisation · AUFederal University of Technology · NGFisher College · US

Funding

INTERDISCIPLINARY TRAINING IN THE CHEMICAL SENSEST32DC000014 · NIDCD · MONELL CHEMICAL SENSES CENTER · PI PAMELA HELEN DALTON, Joel D Mainland · 1989 to 2026
$6.2M
Sensory Science and Metabolism; Molecular and Neuronal MechanismsZIANR000035 · NINR · NATIONAL INSTITUTE OF NURSING RESEARCH · PI JOSEPH, PAULE · 2018 to 2020
$1.7M
SCENTinel: A Rapid Smell Test for COVID-19 SurveillanceU01DC019578 · NIDCD · MONELL CHEMICAL SENSES CENTER · PI DALTON, PAMELA HELEN, PARMA, VALENTINA · 2021 to 2023
$1.2M
NIDCD NIH HHS T32 DC000014NIDCD NIH HHS U01 DC019578
6 · The paper itself

Abstract

backgroundCOVID-19 has heterogeneous manifestations, though one of the most common symptoms is a sudden loss of smell (anosmia or hyposmia). We investigated whether olfactory loss is a reliable predictor of COVID-19.

methodsThis preregistered, cross-sectional study used a crowdsourced questionnaire in 23 languages to assess symptoms in individuals self-reporting recent respiratory illness. We quantified changes in chemosensory abilities during the course of the respiratory illness using 0-100 visual analog scales (VAS) for participants reporting a positive (C19+; n=4148) or negative (C19-; n=546) COVID-19 laboratory test outcome. Logistic regression models identified singular and cumulative predictors of COVID-19 status and post-COVID-19 olfactory recovery.

resultsBoth C19+ and C19- groups exhibited smell loss, but it was significantly larger in C19+ participants (mean±SD, C19+: -82.5±27.2 points; C19-: -59.8±37.7). Smell loss during illness was the best predictor of COVID-19 in both single and cumulative feature models (ROC AUC=0.72), with additional features providing no significant model improvement. VAS ratings of smell loss were more predictive than binary chemosensory yes/no-questions or other cardinal symptoms, such as fever or cough. Olfactory recovery within 40 days was reported for ~50% of participants and was best predicted by time since illness onset.

conclusionsAs smell loss is the best predictor of COVID-19, we developed the ODoR-19 tool, a 0-10 scale to screen for recent olfactory loss. Numeric ratings ≤2 indicate high odds of symptomatic COVID-19 (10<OR<4), especially when viral lab tests are impractical or unavailable.

Identifiers

PMID32743605
PMCPMC7386529
OpenAlexW3044509517

What OpenQuestion holds

Textmetadata
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.