Facemask and Respirator in Reducing the Spread of Respiratory Viruses; a Systematic Review.
Bibliographic record
Abstract
INTRODUCTION: Respiratory viruses spread fast, and some manners have been recommended for reducing the spread of these viruses, including the use of a facemask or respirator, maintaining hand hygiene, and perfoming social distancing. This systematic review aimed to assess the impact of facemasks and respirators on reducing the spread of respiratory viruses. METHODS: We conducted a systematic review using MeSH terms, and reported findings according to PRISMA. PubMed, Embase, Cochrane Library, Scopus, ProQuest, Web of Science(WoS), and Google Scholar were searched for articles published between 2009 and 2020. Two independent reviewers determined whether the studies met inclusion criteria. The risk of bias of studies was assessed using Newcastle-Ottawa (NOS) and Consolidated Standards of Reporting Trials (CONSORT). RESULTS: A total of 1505 articles were initially retrieved and 10 were finally included in our analysis (sample size: 3065). 96.8% of non-infected participants used facemask or respirator in contact with people infected with a respiratory virus, facemask and respirator have a significant effect on reducing the spread of respiratory viruses. CONCLUSION: Evidence support that using a facemask or respirator can reduce the spread of all types of respiratory viruses; therefore, this result can be generalized to the present pandemic of a respiratory virus (SARS-COV-2) and it is recommended to use a facemask or respirator for reducing the spread of this respiratory virus.
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 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 | 0.010 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".