Forgotten Technology in the COVID-19 Pandemic: Filtration Properties of Cloth and Cloth Masks—A Narrative Review
Bibliographic record
Abstract
Management of the global crisis of the coronavirus disease 2019 pandemic requires detailed appraisal of evidence to support clear, actionable, and consistent public health messaging. The use of cloth masks for general public use is being debated, and is in flux. We searched the MEDLINE and EMBASE databases and Google for articles reporting the filtration properties of flat cloth or cloth masks. We reviewed the reference lists of relevant articles to identify further articles and identified articles through social and conventional news media. We found 25 articles. Study of protection for the wearer used healthy volunteers, or used a manikin wearing a mask, with airflow to simulate different breathing rates. Studies of protection of the environment, also known as source control, used convenience samples of healthy volunteers. The design and execution of the studies was generally rigorously described. Many descriptions of cloth lacked the detail required for reproducibility; no study provided all the expected details of material, thread count, weave, and weight. Some of the homemade mask designs were reproducible. Successful masks were made of muslin at 100 threads per inch (TPI) in 3 to 4 layers (4-layer muslin or a muslin-flannel-muslin sandwich), tea towels (also known as dish towels), made using 1 layer (2 layers would be expected to be better), and good-quality cotton T-shirts in 2 layers (with a stitched edge to prevent stretching). In flat-cloth experiments, linen tea towels, 600-TPI cotton in 2 layers, and 600-TPI cotton with 90-TPI flannel performed well but 80-TPI cotton in 2 layers did not. We therefore recommend cotton or flannel at least 100 TPI, at least 2 layers. More layers, 3 or 4, will provide increased filtration but there is a trade-off in that more layers increases the resistance to breathing. Although this is not a systematic review, we included all the articles that we identified in an unbiased way. We did not include gray literature or preprints. A plain language summary of these data and recommendations, as well as information on making, wearing and cleaning cloth masks is available at www.clothmasks.ca.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".