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Record W3092810699 · doi:10.7916/vib.v6i.5896

The precautionary principle in mask-wearing

2020· article· en· W3092810699 on OpenAlexaboutno aff
Anne Zimmerman

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2020
Typearticle
Languageen
FieldMedicine
TopicInfection Control and Ventilation
Canadian institutionsnot available
Fundersnot available
KeywordsPrecautionary principlePsychologyComputer science

Abstract

fetched live from OpenAlex

Science Magazine interview with George Gao, director-general of the Chinese Center for Disease Control and Prevention (CDC):[1] “Q: What mistakes are other countries making? A: The big mistake in the U.S. and Europe, in my opinion, is that people aren’t wearing masks. This virus is transmitted by droplets and close contact. Droplets play a very important role—you’ve got to wear a mask, because when you speak, there are always droplets coming out of your mouth. Many people have asymptomatic or presymptomatic infections. If they are wearing face masks, it can prevent droplets that carry the virus from escaping and infecting others.” China and Hong Kong have specific universal mask recommendations for the general population, mostly concerning those in crowded places or at increased risk. Early in the pandemic in the US, there was a bright line claim that seemed suspicious to me: masks that are not N95 offer no protection. Another follow up claim: masks are only to prevent the spread by the wearer and do not protect the wearer. Neither claim is exactly proven and the veracity with which they were spoken may have undermined protection. The absence of incontrovertible scientific studies supporting widespread mask use is not the same as evidence that masks are ineffective. There is not evidence that masks worn by the general public are ineffective. However, many US scientists have made that very claim. Healthcare professionals who publicly said masks are ineffective or not recommended include: Jerome Adams, US Surgeon General; the US CDC; David Heymann, epidemiologist with WHO at the time of SARS (because of fear of improper use); Emily Landon, University of Chicago Medical School (because of improper use and because N95 are the only masks prove effective)[2]; Hyo-Jick Choi, Chemical Engineer, University of Alberta (because masks are for large droplets only); Eric Toner, Johns Hopkins (“no harm in it but it is not likely to be very effective”) [3]; Alax Azar, HHS secretary, (masks should be just for healthcare workers); Amesh Adalja, Johns Hopkins (masks may give false sense of security); Nancy Messonier, CDC.[4] This list could continue as others echoed theses sentiments. From the beginning, I felt people on the subway or in crowded neighborhoods were smart to wear a mask. If I said that out loud, medical people would say “no evidence of that.” Their logic bothered me for two reasons: it was a failure to play it safe (could we not recommend those over 65 or immune compromised wear one just in case it helps?), and the claimed ineffectiveness seemed not to have been proven. Furthermore, the harms of wearing an ill-fitting mask or removing it incorrectly have been overstated without proof. It would be simple to demonstrate proper mask removal in a similar manner to the new CDC commercials on television instructing basic protective techniques. While no one wants to advocate civilians buying the limited supply of specialized respiratory masks like the N95 mask, the stance that no other masks serve any protective benefits is incorrect and unhelpful. Some suggest a need to prevent a false sense of security that would encourage people, whether healthcare workers or not, to behave otherwise recklessly. Were we all going to ignore social distancing because the mask made us feel so secure? Aside from healthcare workers, it is doubtful the people in masks would be the people taking risks. Those not following recommended procedures for social distancing or hand washing are probably not wearing masks. While most would continue their safety habits, even if some slacked off momentarily, for example, in the check-out line at a store, the mask might be helpful. While so many doctors and public health advocates in the US took the anti-mask for prevention stance, in other countries mask-wearing is recommended and some scientists are even recommending scarves in public to cover the nose and mouth. There is some evidence that wearing a mask can be outcome-determinative. In 2008, one review of controlled studies of SARS demonstrated that masks were protective[5] alone and were more protective when part of a regimen of hand-washing, and wearing gloves and gowns, a finding more relevant to healthcare situations than public ones. A 2013 study found surgical masks somewhat protective and possibly worthwhile when better ones are not available for healthcare workers to prevent spread of influenza.[6] While data did generate numbers to estimate prevention in lives saved or disease prevented, studies of mask use by the public require largescale epidemiology and may need to be retrospective. Finally, today a journal article agrees with me.[7] In the Lancet, the authors argue that recommendations on masks should be rational and that recommending universal mask wearing is within logic if there is availability. After recommending masks for the vulnerable, those with preexisting conditions, and older adults, the article concludes, “Universal use of face masks could be considered if supplies permit.” March 30, 2020 Photo by Mika Baumeister on Unsplash [1] Cohen, Jon. “Not wearing masks to protect against coronavirus is a ‘big mistake,’ top Chinese scientist says,” Science Magazine. Mar. 27, 2020. See also https://www.nytimes.com/2020/03/27/health/us-coronavirus-face-masks.html [2] https://www.cnbc.com/2020/03/02/coronavirus-do-face-masks-work-and-how-to-stop-it-from-spreading.html [3] https://www.businessinsider.com/coronavirus-face-mask-safe-prevention-2020-2 [4] https://www.marketwatch.com/story/the-cdc-says-americans-dont-have-to-wear-facemasks-because-of-coronavirus-2020-01-30 [5] Jefferson, Tom et al. “Physical interventions to interrupt or reduce the spread of respiratory viruses: systematic review.” BMJ (Clinical research ed.) vol. 336,7635 (2008): 77-80. doi:10.1136/bmj.39393.510347.BE [6] Makison, C., et al, “Effectiveness of surgical masks against influenza bioaerosols,” Journal of Hospital Infection Vol. 84, No. 1, May 2013. https://www.sciencedirect.com/science/article/abs/pii/S0195670113000698 [7] Feng, Shuo, et al. “Rational use of face masks in the COVID-19 pandemic” The Lancet. Published: March 20, 2020DOI:https://doi.org/10.1016/S2213-2600(20)30134-X

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.025
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.018
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0110.018
Scholarly communication0.0050.007
Open science0.0020.004
Research integrity0.0250.023
Insufficient payload (model declined to judge)0.0060.002

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.

Opus teacher head0.272
GPT teacher head0.557
Teacher spread0.286 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreCommentary

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".

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Citations1
Published2020
Admission routes1
Has abstractyes

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