Correction: The Sensitivity and Costs of Testing for SARS-CoV-2 Infection With Saliva Versus Nasopharyngeal Swabs
Bibliographic record
Abstract
CorrectionsApril 2021Correction: The Sensitivity and Costs of Testing for SARS-CoV-2 Infection With Saliva Versus Nasopharyngeal SwabsFREEThis correction concerns the following article:ReviewsApr 2021The Sensitivity and Costs of Testing for SARS-CoV-2 Infection With Saliva Versus Nasopharyngeal SwabsFREEMayara Lisboa Bastos, MD, Sara Perlman-Arrow, Dick Menzies, MD, and Jonathon R. Campbell, PhDMayara Lisboa Bastos, MDMcGill University and McGill International TB Centre, Montreal, Quebec, Canada, and State University of Rio de Janeiro, Rio de Janeiro, Brazil (M.L.B.), Sara Perlman-ArrowMcGill University, Montreal, Quebec, Canada (S.P.), Dick Menzies, MDMcGill University, McGill International TB Centre, and Montreal Chest Institute, Montreal, Quebec, Canada (D.M.), and Jonathon R. Campbell, PhDMcGill University and McGill International TB Centre, Montreal, Quebec, Canada (J.R.C.).https://doi.org/10.7326/L21-0055 SectionsAboutVisual AbstractPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail In the review by Bastos and colleagues (1), a labeling error in the Figure has been corrected. All estimates remain unchanged and valid. These corrections were made after review of a comment by readers (2).These corrections have been made online.References1. Bastos ML, Perlman-Arrow S, Menzies D, et al. The sensitivity and costs of testing for SARS-CoV-2 infection with saliva versus nasopharyngeal swabs. A systematic review and meta-analysis. Ann Intern Med. 2021;174:501-10. doi:10.7326/M20-6569 LinkGoogle Scholar2. Hill C, Thuret J. The sensitivity and costs of testing for SARS-CoV-2 infection with saliva versus nasopharyngeal swabs [Letter]. Ann Intern Med. 2021;174:582. doi:10.7326/L21-0092 Google Scholar Comments0 CommentsSign In to Submit A Comment PreviousarticleNextarticle Advertisement FiguresReferencesRelatedDetailsSee AlsoThe Sensitivity and Costs of Testing for SARS-CoV-2 Infection With Saliva Versus Nasopharyngeal Swabs Mayara Lisboa Bastos , Sara Perlman-Arrow , Dick Menzies , and Jonathon R. Campbell Metrics Cited byReal-life experience: sensitivity and specificity of nasal and saliva samples for COVID-19 diagnosis April 2021Volume 174, Issue 4Page: 584 ePublished: 20 April 2021 Issue Published: April 2021 Copyright & PermissionsCopyright © 2021 by American College of Physicians. All Rights Reserved.PDF downloadLoading ...
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.176 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.090 | 0.044 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".