Masking for COVID-19 and other respiratory viral infections: implications of the available evidence
Bibliographic record
Abstract
The use of face masks has been widely promoted and at times mandated to prevent coronavirus disease 2019 (COVID-19). The 2023 publication of an updated Cochrane review on mask effectiveness for respiratory viruses as well as the unfolding epidemiology of COVID-19 underscore the need for an unbiased assessment of the current scientific evidence. It appears that the widespread promotion, adoption, and mandating of masking for COVID-19 were based not primarily on the strength of evidence for effectiveness but more on the imperative of decision-makers to act in the face of a novel public health emergency, with seemingly few good alternatives. Randomized clinical trials of masking for prevention of COVID-19 and other respiratory viruses have so far shown no evidence of benefit (with the possible exception of continuous use of N95 respirators by hospital workers). Observational studies provide lower-quality evidence and do not convincingly demonstrate benefit from masking or mask mandates. Unless robust new evidence emerges showing the effectiveness of masks in reducing infection or transmission risks in either trials or real-world conditions, mandates are not warranted for future epidemics of respiratory viral infections.
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 | 0.021 | 0.103 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.014 | 0.017 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".