Serodiagnostics for SARS-CoV-2
Notice bibliographique
Résumé
LettersFebruary 2021Serodiagnostics for SARS-CoV-2FREEJesse Papenburg, MD, MSc, Cedric P. Yansouni, MD, Chelsea Caya,, MScPH, Matthew P. Cheng, MDCMJesse Papenburg, MD, MScMcGill Interdisciplinary Initiative in Infection and Immunity, School of Population and Global Health, McGill University, and Montreal Children's Hospital, Montreal, Quebec, Canada, Cedric P. Yansouni, MDMcGill University Health Centre, McGill Interdisciplinary Initiative in Infection and Immunity, and J.D. MacLean Centre for Tropical Diseases, McGill University, Montreal, Quebec, Canada, Chelsea Caya,, MScPHMcGill Interdisciplinary Initiative in Infection and Immunity, Montreal, Quebec, Canada, Matthew P. Cheng, MDCMMcGill University Health Centre and McGill Interdisciplinary Initiative in Infection and Immunity, Montreal, Quebec, CanadaAuthor, Article, and Disclosure Informationhttps://doi.org/10.7326/L20-1396 SectionsAboutVisual AbstractPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail IN RESPONSE:We would like to clarify our statement on the importance of accounting for the prevalence of prior severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in the population when interpreting serologic test results. We apologize that we unintentionally omitted the value of test sensitivity necessary for calculation of the example, for which we were using an estimate of 95%. Furthermore, the use of the term “false-positive rate” may have led to confusion, because it is sometimes used to refer to (1 − specificity) (1). However, we were using a common statistical definition of this term, whereby “the false positive rate [emphasis added] is the percentage of people who test positive but do not actually have the disease” (2).Nevertheless, the key concept expressed in this section of our review is that imperfect test specificity can introduce important bias when prior infection prevalence is very low, where the number of false-positive results could equal or even outnumber true-positive results. Drs. Harada Sassa and Harada have also explored this issue in their own work using lateral-flow and enzyme-linked immunosorbent assays on pre–coronavirus disease 2019 sera in Japan, showing the risk of overestimating SARS-CoV-2 seroprevalence when using these tools (3). Investigators should be encouraged to use analytical methods that adjust for imperfect test sensitivity and specificity and disease prevalence when estimating SARS-CoV-2 seroprevalence (4).References1. Casscells W, Schoenberger A, Graboys TB. Interpretation by physicians of clinical laboratory results. N Engl J Med. 1978;299:999-1001. [PMID: 692627] CrossrefMedlineGoogle Scholar2. King AP, Eckersley RJ. Descriptive Statistics III: ROC Analysis. In: King AP, Eckersley RJ, eds. Statistics for Biomedical Engineers and Scientists. How to Visualize and Analyze Data: Academic Pr; 2019:57-69. Google Scholar3. Lyu Z, Harada Sassa, Fujitani T, et al. Serological tests for SARS-CoV-2 coronavirus by commercially available point-of-care and laboratory diagnostics in pre-COVID-19 samples in Japan. Diseases. 2020;8. [PMID: 32977485] doi:10.3390/diseases8040036 CrossrefMedlineGoogle Scholar4. Clapham H, Hay J, Routledge I, et al. Seroepidemiologic study designs for determining SARS-COV-2 transmission and immunity. Emerg Infect Dis. 2020;26:1978-86. [PMID: 32544053] doi:10.3201/eid2609.201840 CrossrefMedlineGoogle Scholar Comments0 CommentsSign In to Submit A Comment Author, Article, and Disclosure InformationAuthors: Jesse Papenburg, MD, MSc; Cedric P. Yansouni, MD; Chelsea Caya,, MScPH; Matthew P. Cheng, MDCMAffiliations: McGill Interdisciplinary Initiative in Infection and Immunity, School of Population and Global Health, McGill University, and Montreal Children's Hospital, Montreal, Quebec, CanadaMcGill University Health Centre, McGill Interdisciplinary Initiative in Infection and Immunity, and J.D. MacLean Centre for Tropical Diseases, McGill University, Montreal, Quebec, CanadaMcGill Interdisciplinary Initiative in Infection and Immunity, Montreal, Quebec, CanadaMcGill University Health Centre and McGill Interdisciplinary Initiative in Infection and Immunity, Montreal, Quebec, CanadaDisclosures: Disclosures can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=M20-2854. PreviousarticleNextarticle Advertisement FiguresReferencesRelatedDetailsSee AlsoSerodiagnostics for Severe Acute Respiratory Syndrome–Related Coronavirus 2 Matthew P. Cheng , Cedric P. Yansouni , Nicole E. Basta , Michaël Desjardins , Sanjat Kanjilal , Katryn Paquette , Chelsea Caya , Makeda Semret , Caroline Quach , Michael Libman , Laura Mazzola , Jilian A. Sacks , Sabine Dittrich , and Jesse Papenburg Serodiagnostics for SARS-CoV-2 Mariko Harada Sassa , Kouji H. Harada Metrics Cited byTwo‐phase Bayesian latent class analysis to assess diagnostic test performance in the absence of a gold standard: COVID ‐19 serological assays as a proof of conceptDiagnostic accuracy of rapid one-step PCR assays for detection of herpes simplex virus-1 and -2 in cerebrospinal fluid: a systematic review and meta-analysis February 2021Volume 174, Issue 2Page: 287-288KeywordsCOVID-19DisclosureEnzyme linked immunosorbent assaySpecificityUpper respiratory tract infections ePublished: 16 February 2021 Issue Published: February 2021 Copyright & PermissionsCopyright © 2021 by American College of Physicians. All Rights Reserved.PDF downloadLoading ...
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,021 | 0,007 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».