Are cloth masks a substitute to medical masks in reducing transmission and contamination? A systematic review
Bibliographic record
Abstract
During the COVID-19 pandemic the use of cloth masks has increased dramatically due to the shortage of medical masks. However, the efficiency of this material is controversial. We aimed to investigate the efficiency of cloth masks in reducing transmission and contamination by droplets and aerosols for the general population and healthcare workers. Electronic databases were searched without year or language restrictions. Clinical and laboratorial studies were included. The risk of bias (RoB) was assessed using an adapted quality checklist for laboratory-based studies. ROBINS-I tool and Cochrane RoB 2.0 were used to evaluate non-randomized (n-RCT) and randomized clinical trials (RCT), respectively. The quality of the evidence was assessed through GRADE tool. From the eleven studies selected, eight were laboratory-based studies, one non-randomized and one RCT supported by laboratory data. Between the evaluated fabrics only three presented a filtration efficiency > 90%. Hybrid of cotton/chiffon (95%CI 95.2 to 98.8), hybrid of cotton/silk (95%CI 92.2 to 95.8) and cotton quilt (95%CI 94.2 to 97.8). However, cloth masks are not recommended for healthcare workers. A meta-analysis was not feasible due to a high methodological heterogeneity. The overall quality of evidence ranged from very low to moderate. Despite the lower efficiency compared to medical masks, laboratorial results may underestimate the efficiency of cloth masks in real life. Cloth mask efficiency is higher when made of hybrid fabrics (cotton/chiffon, cotton/silk) and cotton quilt, mainly with multiple layers.
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 | 0.009 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.010 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".