Comparative effectiveness of N95, surgical or medical, and non‐medical facemasks in protection against respiratory virus infection: A systematic review and network meta‐analysis
Bibliographic record
Abstract
The aim of this systematic review and network meta-analysis is to evaluate the comparative effectiveness of N95, surgical/medical and non-medical facemasks as personal protective equipment against respiratory virus infection. The study incorporated 35 published and unpublished randomized controlled trials and observational studies investigating specific mask effectiveness against influenza virus, SARS-CoV, MERS-CoV and SARS-CoV-2. We searched PubMed, Google Scholar and medRxiv databases for studies published up to 5 February 2021 (PROSPERO registration: CRD42020214729). The primary outcome of interest was the rate of respiratory viral infection. The quality of evidence was estimated using the GRADE approach. High compliance to mask-wearing conferred a significantly better protection (odds ratio [OR], 0.43; 95% confidence interval [CI], 0.23-0.82) than low compliance. N95 or equivalent masks were the most effective in providing protection against coronavirus infections (OR, 0.30; CI, 0.20-0.44) consistently across subgroup analyses of causative viruses and clinical settings. Evidence supporting the use of medical or surgical masks against influenza or coronavirus infections (SARS, MERS and COVID-19) was weak. Our study confirmed that the use of facemasks provides protection against respiratory viral infections in general; however, the effectiveness may vary according to the type of facemask used. Our findings encourage the use of N95 respirators or their equivalents (e.g., P2) for best personal protection in healthcare settings until more evidence on surgical and medical masks is accrued. This study highlights a substantial lack of evidence on the comparative effectiveness of mask types in community settings.
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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 | 0.017 | 0.040 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.041 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".