Evidence map›Paper›PMID 32530131›Full record

ArticleHead & neck2020

False-positive reverse transcriptase polymerase chain reaction screening for SARS-CoV-2 in the setting of urgent head and neck surgery and otolaryngologic emergencies during the pandemic: Clinical implications.

Andrew P Katz, Francisco J Civantos, Zoukaa Sargi, Jason M Leibowitz, Elizabeth A Nicolli, Donald Weed, Alexander E Moskovitz, Alyssa M Civantos, David M Andrews, Octavio Martinez and 1 more

2 registry-linked trialsAbstract read
In one paragraph

Article in Head & neck, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 27 papers.

0numbers the graph read from it
0cells of the map it votes in
27citing 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.

NCT05175690 completednot on this mapstarted 2022, after this paper: background citation

Evaluation of the Artificial Intelligence/Machine Learning-Based Diagnostic Software as a Medical Device Using Forced Cough Vocalization Signal Data Signatures in the Diagnosis of COVID-19 Illness

TypeobservationalSponsorAudibleHealth AI, Inc.Ran2022 to 2022Enrolled1,126ConditionsCOVID19ArmsDiagnostic Software as Medical Device
NCT05364268 completednot on this mapstarted 2022, after this paper: background citation

Evaluation of the Artificial Intelligence/Machine Learning-Based Diagnostic Software as a Medical Device Using Forced Cough Vocalization Signal Data Signatures in the Diagnosis of COVID-19 Illness: A Prospective, Two-Arm Non-Inferiority Clinical Validation Trial of AudibleHealth Dx Software as a Medical Device (EUA-US)

TypeobservationalSponsorAudibleHealth AI, Inc.Ran2022 to 2022Enrolled514Conditions2019 Novel Coronavirus Disease, 2019 Novel Coronavirus Infection, 2019-nCoV Disease, COVID-19 PandemicArmsDiagnostic Test: Diagnostic Software as Medical Device
3 · Its place in the literature

Who cites it

27 citing papers in PubMed.

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  8. The utility of SARS-CoV-2-specific serology in COVID-19 diagnosis.Australian and New Zealand journal of public health · 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

11 authors.

Andrew P KatzDepartment of Otolaryngology, University of Miami Miller School of Medicine, Miami, Florida, USA.ORCID https://orcid.org/0000-0002-8345-6163
Francisco J CivantosDepartment of Otolaryngology, University of Miami Miller School of Medicine, Miami, Florida, USA.ORCID https://orcid.org/0000-0002-1648-0868
Zoukaa SargiDepartment of Otolaryngology, University of Miami Miller School of Medicine, Miami, Florida, USA.
Jason M LeibowitzDepartment of Otolaryngology, University of Miami Miller School of Medicine, Miami, Florida, USA.
Elizabeth A NicolliDepartment of Otolaryngology, University of Miami Miller School of Medicine, Miami, Florida, USA.
Alexander E MoskovitzDepartment of Otolaryngology, University of Miami Miller School of Medicine, Miami, Florida, USA.
Alyssa M CivantosPerelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA.ORCID https://orcid.org/0000-0003-1226-6634
David M AndrewsDepartment of Pathology, University of Miami Miller School of Medicine, Miami, Florida, USA.
Octavio MartinezDepartment of Pathology, University of Miami Miller School of Medicine, Miami, Florida, USA.
Giovana R ThomasDepartment of Otolaryngology, University of Miami Miller School of Medicine, Miami, Florida, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundNo reports describe falsepositive reverse transcriptase polymerase chain reaction (RT-PCR) for novel coronavirus in preoperative screening.

methodsPreoperative patients had one or two nasopharyngeal swabs, depending on low or high risk of viral transmission. Positive tests were repeated.

resultsForty-three of 52 patients required two or more preoperative tests. Four (9.3%) had discrepant results (positive/negative). One of these left the coronavirus disease (COVID) unit against medical advice despite an orbital abscess, with unknown true disease status. The remaining 3 of 42 (7.1%) had negative repeat RT-PCR. Although ultimately considered falsepositives, one was sent to a COVID unit postoperatively and two had urgent surgery delayed. Assuming negative repeat RT-PCR, clear chest imaging, and lack of subsequent symptoms represent the "gold standard," RT-PCR specificity was 0.97.

conclusionsIf false positives are suspected, we recommend computed tomography (CT) of the chest and repeat RT-PCR. Validated serum immunoglobulin testing may ultimately prove useful.

Indexed as

False Positive ReactionsOtorhinolaryngologic Surgical ProceduresReverse Transcriptase Polymerase Chain ReactionAged, 80 and overBetacoronavirusCoronavirus InfectionsCOVID-19EmergenciesFemaleFloridaHumansLungMaleMiddle AgedNasopharynxPandemicsRNA, ViralCOVID-19head and neck surgerypandemicpreoperative testingRT-PCR

Identifiers

PMID32530131
PMCPMC7307014

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

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.