Bibliographic record
Abstract
LettersApril 2021Cloth Masks May Prevent Transmission of COVID-19FREEMaxwell G. Anderson, PhDMaxwell G. Anderson, PhDVancouver, British Columbia, CanadaSearch for more papers by this authorAuthor, Article, and Disclosure Informationhttps://doi.org/10.7326/L21-0090 SectionsAboutVisual AbstractPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail TO THE EDITOR: In a comment on Clase and colleagues' commentary (1), FitzGerald cites Bae and associates' conclusion that “both surgical and cotton masks seem to be ineffective in preventing the dissemination of SARS-CoV-2 from the coughs of patients with COVID-19 to the environment and external mask surface” (2). Of note, Bae and associates' brief research report was retracted on 2 June 2020. Further, my analysis of Bae and associates' published data indicates that filtration efficiency with patients coughing into their masks averaged approximately 76% or possibly slightly lower because of their limits of detection. Surgical masks are often found to have 98% to 99% virus filtration efficiency under ideal conditions and approximately 80% in actual practice, with variation among brands, models, and wearers. As such, Bae and associates' data were within normal expectations in that regard. Unfortunately, more than 80 news outlets repeated Bae and associates' claim that masks were “ineffective.”Clase and colleagues encourage mask wearing while stating, “There is absence of evidence that public mask wearing protects either the wearer or others” (3). However, Leffler and coworkers' analysis (4) published on 23 April 2020 is evidence that public mask wearing reduced COVID-19 by an order of magnitude.References1. Clase CM, Fu EL, Joseph M, et al. Cloth masks may prevent transmission of COVID-19: an evidence-based, risk-based approach [Editorial]. Ann Intern Med. 2020;173:489-91. [PMID: 32441991]. doi: 10.7326/M20-2567 LinkGoogle Scholar2. Bae S, Kim MC, Kim JY, et al. Effectiveness of surgical and cotton masks in blocking SARS-CoV-2: a controlled comparison in 4 patients [Letter]. Ann Intern Med. 2020;173:W22-3. [PMID: 32251511]. doi: 10.7326/M20-1342 LinkGoogle Scholar3. Clase CM, Fu EL, Jardine M, et al. Re: Cloth masks may prevent transmission of COVID-19: an evidence-based, risk-based approach. Online comment. Annals.org. 2 February 2021. Accessed at www.annals.org/doi/10.7326/M20-2567 on 17 February 2021. Google Scholar4. Leffler CT, Ing E, Lykins JD, et al. Country-wide coronavirus mortality and use of masks by the public. ResearchGate. Preprint posted online 23 April 2020. doi:10.13140/RG.2.2.35208.37125 Google Scholar Comments 0 Comments Sign In to Submit A Comment Author, Article, and Disclosure InformationAuthors: Maxwell G. Anderson, PhDAffiliations: Vancouver, British Columbia, CanadaDisclosures: The author has reported no disclosures of interest. The form can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=L21-0090. PreviousarticleNextarticle Advertisement FiguresReferencesRelatedDetailsSee AlsoCloth Masks May Prevent Transmission of COVID-19: An Evidence-Based, Risk-Based Approach Catherine M. Clase , Edouard L. Fu , Meera Joseph , Rupert C.L. Beale , Myrna B. Dolovich , Meg Jardine , Johannes F.E. Mann , Roberto Pecoits-Filho , Wolfgang C. Winkelmayer , and Juan J. Carrero Cloth Masks May Prevent Transmission of COVID-19 Catherine M. Clase , Edouard L. Fu , Meg Jardine , Johannes F.E. Mann , Juan J. Carrero Cloth Masks May Prevent Transmission of COVID-19 Kouji H. Harada , Mariko Harada Sassa Cloth Masks May Prevent Transmission of COVID-19 George FitzGerald Metrics Cited byA Miniaturized Electrostatic Precipitator Respirator Effectively Removes Ambient SARS-CoV-2 Bioaerosols April 2021Volume 174, Issue 4 Page: 579-580 ePublished: 20 April 2021 Issue Published: April 2021 Copyright & PermissionsCopyright © 2021 by American College of Physicians. All Rights Reserved.PDF downloadLoading ...
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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 teacher head, 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".